Supplmental reproducible analysis for: Genetically encoded, noise-tolerant, auxin biosensors in yeast facilitate metabolic engineering and directed evolution
Patarasuda Chaisupa1
Mahbubur Rahman*2
Sherry B. Hildreth3
Saede Moseley4
Chauncey Gatling5
Richard F. Helm6
R. Clay Wright7
knitr::opts_chunk$set(echo = TRUE, collapse = TRUE,
tidy = TRUE, message = FALSE,
warning = FALSE)
library(flowCore)
library(flowTime)
library(ggplot2)
library(stringr)
library(ggridges)
library(dplyr)
library(tidyr)
library(tidyverse)
library(drc)
library(gridExtra)
library(openCyto)
library(ggcyto)
library(flowStats)
library(flowClust)
library(wesanderson)
library(patchwork)
library(ggthemes)
library(agricolae)Time-course response and ratiometric measurement of the single-fusion biosensors
Time-course degradation
# Read in flow sets from 20200611 and 20200614
flowSet <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Data/Mahbub/FlowSets/", pattern = "202006", phenoData = "annotation.txt")
flowSet <- flowSet[which(flowSet@phenoData@data$strain %in% c("T1T1", "A2A2"))]
write.flowSet(flowSet, "flowSets/single-time-course")
flowSet <- read.flowSet(path = "flowSets/single-time-course", phenoData = "annotation.txt")
# load gates for this strain/cytometer
load("PSB_Accuri_W303.RData")
data_sum <- summarizeFlow(flowset = flowSet, ploidy = "diploid", only = "singlets")[1] "Gating with diploid singlet gates..."time0_14 <- data_sum %>% dplyr::filter(name == "21D10.fcs") %>%
pull(btime)
time0_11 <- data_sum %>% dplyr::filter(name == "11G02.fcs") %>%
pull(btime)
data_sum <-
data_sum %>%
mutate(time = case_when(
folder == "20200611_AFB_epistasis" ~
.$btime - time0_11,
folder == "20200614_AFB_epistasis" ~
.$btime - time0_14
))shapes <- c("DMSO" = 1, "IAA" = 16)
lines <- c("DMSO" = 2, "IAA" = 1)
data_sum$ratio <- data_sum$FL1.Amean/data_sum$FL4.Amean
data_sum$Venus <- data_sum$FL1.Amean/10000
data_sum_long <- data_sum %>%
dplyr::select(time, treatment, yWL, folder, strain, ratio, Venus) %>%
pivot_longer(cols = c(ratio, Venus), names_to = "parameter")
deg_plot <- ggplot(data = subset(data_sum_long, yWL == "166"),
aes(x = time, y = value, shape = treatment,
color = treatment,
group = interaction(treatment, folder))) +
geom_point() + labs(y = "intensity (AU)", x = "time post auxin addition (min)") +
facet_wrap(~fct_rev(parameter), scales = "free") +
scale_shape_manual(values = shapes) +
geom_line(aes(linetype = treatment)) +
scale_linetype_manual(values = lines) +
scale_color_viridis_d(option = "D", end = 0.75, direction = -1) +
theme_test()
deg_plot
data <- flowTime::tidyFlow(flowSet, ploidy = "diploid", only = "singlets")[1] "No further gating applied."
[1] "Converting events..."# get late time points for one strain
last_reading <-
tail(unique(data[which(data$yWL == 166), "name"]),4) %>% head(2)
data <- dplyr::filter(data, yWL == "166" & name %in% last_reading)
# clean this up, cut off zeros
data <- subset(data, FL1.A >1 & FL4.A > 1)
data$FLratio <- data$FL1.A/data$FL4.A
range(data$FL1.A)[1] 21 697262#calculate cvs
sd(data$FLratio)/mean(data$FLratio)[1] 0.1842678range(data$FLratio)[1] 0.002628614 3.836085188Single-fusion CV plot
#calculate normalized values
data$Venus <- data$FL1.A / median(data$FL1.A)
data$mScarlet <- data$FL4.A / median(data$FL4.A)
data$ratio <- data$FLratio / median(data$FLratio)
# make a tidy, long dataset
data_long <- data %>%
dplyr::select(treatment, Venus, mScarlet, ratio) %>%
pivot_longer(cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value")
# need to also format CVs approriately for annotating
cv <- function(x) return(round(sd(x)/mean(x),2))
CVs <- data %>% group_by(treatment) %>%
summarise(across(where(is_double), cv))
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
pivot_longer(cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value")
CV_plot <- ggplot(data = data_long,
mapping = aes(x = value,
color = treatment)) +
geom_density() + coord_cartesian(x = c(0,2)) +
labs(x = "median normalized intensity", color = "treatment") +
facet_grid(fct_relevel(parameter, "Venus")~.) + theme_test() +
geom_text(data = subset(CVs, treatment == "IAA"),
aes(label = paste0("CV = ", value)),
x = 0.3, y = 2) +
geom_text(data = subset(CVs, treatment == "DMSO"),
aes(label = paste0("CV = ", value)),
x = 1.8, y = 2) +
scale_color_viridis_d(option = "D", end = .75, direction = -1)
CV_plot
layout <- "
AAAAB
CCCCC
"
CV_plot + guide_area() + deg_plot + plot_annotation(tag_levels = "A") +
plot_layout(guides = "collect", heights = c(5, 2), design = layout)
ggsave("ratio-deg.pdf", width = 5, height = 5)ggsave("ratio-deg.png", width = 5, height = 5)
Dose-response curves of dual-fusion AFB2 and TIR1 biosensors
AFB2-based biosensor (yWL210 AFB2 dual-fusion, single ratiometric construct)
plate_all_210 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/Combine Data_yWL210/All data/", pattern = "DRA-*")
annotation <- createAnnotation(yourFlowSet = plate_all_210)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL210_datagated_exJan31.csv')
annotation <-
read.csv(
'~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL210_datagated_exJan31.csv'
)
aplate_all_210 <-
annotateFlowSet(yourFlowSet = plate_all_210,
annotation_df = annotation,
mergeBy = 'name')
head(rownames(pData(aplate_all_210)))
head(pData(aplate_all_210))
write.flowSet(aplate_all_210,
outdir = "flowSets/AFB2-dual-yWL210-dose-response")aplate_all_210 <-
read.flowSet(path = "flowSets/AFB2-dual-yWL210-dose-response",
phenoData = "annotation.txt")
plate_all_210_sum <-
summarizeFlow(aplate_all_210, channel = NA, gated = TRUE)[1] "Summarizing all events..."###Dose-response curve
Comparing log-logistic and Weibull models
(Figure 2 in Ritz (2009))
fitdrc.m1 <-
drm(YL1.Amean / BL1.Amean ~ dose, data = plate_all_210_sum, fct = LL.4())
fitdrc.m2 <-
drm(YL1.Amean / BL1.Amean ~ dose, data = plate_all_210_sum, fct = W1.4())
#fitdrc.m3 <-
# drm(YL1.Amean/BL1.Amean~dose, data=plate_all_210_sum, fct = W2.4())
#Error in drmOpt(opfct, opdfct1, startVecSc, optMethod, constrained, warnVal, :
#Convergence failed
summary(fitdrc.m1)
Model fitted: Log-logistic (ED50 as parameter) (4 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
b:(Intercept) -1.5032417 0.6338695 -2.3715 0.01985 *
c:(Intercept) 0.0526074 0.0020790 25.3043 < 2e-16 ***
d:(Intercept) 0.0845079 0.0021128 39.9976 < 2e-16 ***
e:(Intercept) 0.0403739 0.0159429 2.5324 0.01306 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error:
0.01246335 (90 degrees of freedom)summary(fitdrc.m2)
Model fitted: Weibull (type 1) (4 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
b:(Intercept) -0.7846961 0.2493021 -3.1476 0.002233 **
c:(Intercept) 0.0522487 0.0021769 24.0009 < 2.2e-16 ***
d:(Intercept) 0.0847793 0.0022414 37.8241 < 2.2e-16 ***
e:(Intercept) 0.0189778 0.0111048 1.7090 0.090902 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error:
0.01263587 (90 degrees of freedom)#summary(fitdrc.m3)The 4-parameter log-logistic model has a slightly lower residual standard error.
model.LL4_all_210 <-
drm(YL1.Amean / BL1.Amean ~ dose,
data = plate_all_210_sum,
fct = LL.4(names = c(
"Slope", "Lower Limit", "Upper Limit", "ED50"
)))
plot(
model.LL4_all_210,
broken = TRUE,
type = "none",
lty = 1,
lwd = 5,
xlab = "Extracellular IAA concentration (µM)",
ylab = "Response Signal"
)
#plot(model.LL4_all_210, broken = TRUE, col = "black", add=TRUE)
plot(
model.LL4_all_210,
broken = TRUE,
type = "confidence",
col = "black",
add = TRUE
)
summary(model.LL4_all_210)
Model fitted: Log-logistic (ED50 as parameter) (4 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
Slope:(Intercept) -1.5032417 0.6338695 -2.3715 0.01985 *
Lower Limit:(Intercept) 0.0526074 0.0020790 25.3043 < 2e-16 ***
Upper Limit:(Intercept) 0.0845079 0.0021128 39.9976 < 2e-16 ***
ED50:(Intercept) 0.0403739 0.0159429 2.5324 0.01306 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error:
0.01246335 (90 degrees of freedom)Individual replicate dose-response curves
replicate1_210 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(plate_all_210_sum, replicate == "1"),
fct = LL.4()
)
replicate2_210 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(plate_all_210_sum, replicate == "2"),
fct = LL.4()
)
replicate3_210 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(plate_all_210_sum, replicate == "3"),
fct = LL.4()
)
replicate4_210 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(plate_all_210_sum, replicate == "4"),
fct = LL.4()
)
replicate5_210 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(plate_all_210_sum, replicate == "5"),
fct = LL.4()
)
replicate6_210 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(plate_all_210_sum, replicate == "6"),
fct = LL.4()
)
replicate7_210 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(plate_all_210_sum, replicate == "7"),
fct = LL.4()
)
plot(
replicate1_210,
broken = TRUE,
type = "all",
col = "dark green",
lty = 2
)
plot(
replicate2_210,
broken = TRUE,
add = TRUE,
type = "all",
col = "dark blue",
lty = 2
)plot(
replicate3_210,
broken = TRUE,
add = TRUE,
type = "all",
col = "yellow",
lty = 2
)
plot(
replicate4_210,
broken = TRUE,
add = TRUE,
type = "all",
col = "dark grey",
lty = 2
)plot(
replicate5_210,
broken = TRUE,
add = TRUE,
type = "all",
col = "dark orange",
lty = 2
)
plot(
replicate6_210,
broken = TRUE,
add = TRUE,
type = "all",
col = "brown",
lty = 2
)plot(
replicate7_210,
broken = TRUE,
add = TRUE,
type = "all",
col = "dark red",
lty = 2
)
plot(
model.LL4_all_210,
broken = TRUE,
add = TRUE,
type = "none",
lty = 1,
lwd = 5,
xlab = "Extracellular IAA concentration (µM)",
ylab = "Response Signal"
)#plot(model.LL4_all_210, broken = TRUE, col = "black", add=TRUE)
plot(
model.LL4_all_210,
broken = TRUE,
type = "confidence",
col = "black",
add = TRUE
)
TIR1-based biosensor (yWL209 TIR dual-fusion, single ratiometric construct)
plate_all_209 <-
read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/Combine Data_yWL209/", pattern = "DRA-*") annotation <- createAnnotation(yourFlowSet = plate_all_209)
write.csv(
annotation,
'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL209.csv'
)annotation <-
read.csv(
'~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL209.csv'
)
aplate_all_209 <-
annotateFlowSet(yourFlowSet = plate_all_209,
annotation_df = annotation,
mergeBy = 'name')
head(rownames(pData(aplate_all_209)))
head(pData(aplate_all_209))
write.flowSet(aplate_all_209, outdir = "flowSets/TIR1-dual-yWL209-dose-response")aplate_all_209 <-
read.flowSet(path = "flowSets/TIR1-dual-yWL209-dose-response",
phenoData = "annotation.txt")
dat_sum_overlaydata_209 <-
summarizeFlow(aplate_all_209, gated = TRUE)[1] "Summarizing all events..."###Dose-response curve
model.LL4_rep1_209 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(dat_sum_overlaydata_209, replicate == "1"),
fct = LL.4(names = c(
"Slope", "Lower Limit", "Upper Limit", "ED50"
))
)
plot(
model.LL4_rep1_209,
type = "all",
col = "red",
lty = 1,
lwd = 2,
xlab = "Extracellular IAA concentration (µM)",
ylab = "Red/Green Fluorescent Ratio"
)
plot(
model.LL4_rep1_209,
broken = TRUE,
type = "confidence",
col = "red",
add = TRUE
)
print(summary(model.LL4_rep1_209))
Model fitted: Log-logistic (ED50 as parameter) (4 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
Slope:(Intercept) -0.3671010 0.1395354 -2.6309 0.03901 *
Lower Limit:(Intercept) 0.0960915 0.0019149 50.1808 4.201e-09 ***
Upper Limit:(Intercept) 0.1200258 0.0068403 17.5470 2.198e-06 ***
ED50:(Intercept) 1.4438822 2.8035445 0.5150 0.62496
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error:
0.002705295 (6 degrees of freedom)model.LL4_rep2_209 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(dat_sum_overlaydata_209, replicate == "2"),
fct = LL.4(names = c(
"Slope", "Lower Limit", "Upper Limit", "ED50"
))
)
plot(
model.LL4_rep2_209,
type = "all",
col = "blue",
lty = 1,
lwd = 2,
xlab = "Extracellular IAA concentration (µM)",
ylab = "Red/Green Fluorescent Ratio"
)
plot(
model.LL4_rep2_209,
broken = TRUE,
type = "confidence",
col = "blue",
add = TRUE
)
print(summary(model.LL4_rep2_209))
Model fitted: Log-logistic (ED50 as parameter) (4 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
Slope:(Intercept) -0.4323121 0.1153679 -3.7472 0.005646 **
Lower Limit:(Intercept) 0.0998826 0.0018914 52.8084 1.833e-11 ***
Upper Limit:(Intercept) 0.1372100 0.0062312 22.0197 1.910e-08 ***
ED50:(Intercept) 1.4546642 1.4213107 1.0235 0.336036
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error:
0.003755989 (8 degrees of freedom)model.LL4_rep3_209 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(dat_sum_overlaydata_209, replicate == "3"),
fct = LL.4(names = c(
"Slope", "Lower Limit", "Upper Limit", "ED50"
))
)
plot(
model.LL4_rep3_209,
type = "all",
col = "dark green",
lty = 1,
lwd = 2,
xlab = "Extracellular IAA concentration (µM)",
ylab = "Red/Green Fluorescent Ratio"
)
plot(
model.LL4_rep3_209,
broken = TRUE,
type = "confidence",
col = "dark green",
add = TRUE
)
print(summary(model.LL4_rep3_209))
Model fitted: Log-logistic (ED50 as parameter) (4 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
Slope:(Intercept) -0.4007190 0.2177393 -1.8404 0.1030
Lower Limit:(Intercept) 0.0984103 0.0018186 54.1128 1.509e-11 ***
Upper Limit:(Intercept) 0.1148849 0.0037802 30.3916 1.492e-09 ***
ED50:(Intercept) 0.9199369 1.1883595 0.7741 0.4611
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error:
0.002908608 (8 degrees of freedom)model.LL4_all_209 <-
drm(YL1.Amean / BL1.Amean ~ dose,
data = dat_sum_overlaydata_209,
fct = LL.4(names = c(
"Slope", "Lower Limit", "Upper Limit", "ED50"
)))
plot(
model.LL4_all_209,
type = "all",
col = "black",
lty = 1,
lwd = 3
)
plot(
model.LL4_all_209,
broken = TRUE,
col = "black",
add = TRUE
)plot(
model.LL4_all_209,
broken = TRUE,
type = "confidence",
col = "black",
add = TRUE
)
print(summary(model.LL4_all_209))
Model fitted: Log-logistic (ED50 as parameter) (4 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
Slope:(Intercept) -0.3420811 0.0895742 -3.8190 0.0004353 ***
Lower Limit:(Intercept) 0.0973731 0.0014017 69.4690 < 2.2e-16 ***
Upper Limit:(Intercept) 0.1337956 0.0109331 12.2377 1.972e-15 ***
ED50:(Intercept) 10.9140480 20.3972838 0.5351 0.5954208
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error:
0.004904408 (42 degrees of freedom)replicate1_209 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(dat_sum_overlaydata_209, replicate == "1"),
fct = LL.4()
)
replicate2_209 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(dat_sum_overlaydata_209, replicate == "2"),
fct = LL.4()
)
replicate3_209 <-
drm(
YL1.Amean / BL1.Amean ~ dose,
data = subset(dat_sum_overlaydata_209, replicate == "3"),
fct = LL.4()
)
plot(
replicate1_209,
broken = TRUE,
add = TRUE,
type = "none",
col = "red",
lty = 2
)plot(
replicate2_209,
broken = TRUE,
add = TRUE,
type = "none",
col = "blue",
lty = 2
)
plot(
replicate3_209,
broken = TRUE,
add = TRUE,
type = "none",
col = "dark green",
lty = 2
)
Combined Dose-response curveswith ggplot
pm210 <-
expand.grid(treatment = exp(seq(log(1e-5), log(1e2), length = 1000)))
pm210 <-
cbind(pm210,
predict(model.LL4_all_210, newdata = pm210, interval = "confidence"))
plot210 <-
ggplot(plate_all_210_sum, aes(x = dose, y = YL1.Amean / BL1.Amean)) +
scale_x_log10() + geom_ribbon(
data = pm210,
aes(
x = treatment,
y = Prediction,
ymin = Lower,
ymax = Upper
),
alpha = 0.4
) + geom_line(data = pm210,
aes(x = treatment, y = Prediction),
linewidth = 1.2) + ylab("mScarlet-I/Venus") + xlab("Auxin (IAA, µM)") +
scale_x_log10(labels =
scales::label_number(drop0trailing = TRUE)) +
scale_color_viridis_c() +
geom_point(aes(color = replicate), size = 1, alpha = 0.6) +
theme_classic() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5),
plot.title.position = "plot") +
ggtitle("AFB2")
plot210
pm209 <-
expand.grid(treatment = exp(seq(log(1e-5), log(1e2), length = 1000)))
pm209 <-
cbind(pm209,
predict(model.LL4_all_209, newdata = pm209, interval = "confidence"))
plot209 <-
ggplot(dat_sum_overlaydata_209, aes(x = dose, y = YL1.Amean / BL1.Amean)) +
scale_x_log10() + geom_ribbon(
data = pm209,
aes(
x = treatment,
y = Prediction,
ymin = Lower,
ymax = Upper
),
alpha = 0.4
) + geom_line(data = pm209,
aes(x = treatment, y = Prediction),
linewidth = 1.2) +
ylab("mScarlet-I/Venus") +
xlab("Auxin (IAA, µM)") +
scale_x_log10(labels =
scales::label_number(drop0trailing = TRUE)) +
scale_color_viridis_c() +
geom_point(aes(color = replicate), size = 1, alpha = 0.6) +
theme_classic() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5),
plot.title.position = "plot") +
#ylim(0.095, 0.12) +
ggtitle("TIR1")
plot209
plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A")
ggsave(
plot = plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A"),
filename = "dose-response.pdf",
width = 6,
height = 3
)ggsave(
plot = plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A"),
filename = "dose-response.png",
width = 6,
height = 3
)
LCMS measurements of intracellular auxin
plate_08052022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/08052022_yWL210_DRA-LCMS-R2/Alldata/", pattern = "DRA*") annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/08052022_yWL210_DRA-LCMS-R2/08052022_alldata_annotation.csv')
aplate_08052022 <- annotateFlowSet(yourFlowSet = plate_08052022, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_08052022)))
head(pData(aplate_08052022))
write.flowSet(aplate_08052022, "flowSets/AFB2-dual-DR-LCMS")summary(model.LL4_08052022_LCMS)
Model fitted: Log-logistic (ED50 as parameter) (4 parms)
Parameter estimates:
Estimate Std. Error t-value p-value
Slope:(Intercept) -5.6514e-01 8.9868e-02 -6.2885 4.016e-05 ***
Lower Limit:(Intercept) 3.7324e-02 3.6766e-01 0.1015 0.9208
Upper Limit:(Intercept) 2.6037e+02 2.7474e+02 0.9477 0.3620
ED50:(Intercept) 1.1866e+05 2.1885e+05 0.5422 0.5976
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error:
1.019652 (12 degrees of freedom)Auxin-induced degradation time-course assay for the dual-fusion biosensors in different yeast strains
Two isolated colonies from each strain were selected at random and tested in auxin-induced protein degradation assay. IAA working solution was added to obtain the IAA concentration at 50 µM in each culture. The assay was carried out using ThermoFisher Attune NxT B/Y flow cytometer.
Strains:
- yWL185 (TIR1 dual-fusion in W303)
- yWL186 (AFB2 dual-fusion in W303)
- yWL209 (TIR1 dual-fusion in YPH499)
- yWL210 (AFB2 dual-fusion in YPH499)
plate_03112022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/10readings/onlyPatstrains/", pattern = "TCA*") annotation <- createAnnotation(yourFlowSet = plate_03112022)
write.csv(annotation,'~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/03112022_Time-course assay_10platesPatStrains2.csv')annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/03112022_Time-course assay_10platesPatStrains2.csv')
aplate_03112022 <- annotateFlowSet(yourFlowSet = plate_03112022, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_03112022)))
head(pData(aplate_03112022))
write.flowSet(aplate_03112022, outdir = "flowSets/dual-time-course")aplate_03112022 <-
read.flowSet(path = "flowSets/dual-time-course/", phenoData = "annotation.txt")
plate_03112022_sum <- summarizeFlow(aplate_03112022, gated = TRUE)[1] "Summarizing all events..."plate_03112022_sum <-
plate_03112022_sum %>% mutate(
background_p = case_when(
strain %in% c("yWL185", "yWL186") ~ "W303",
strain %in% c("yWL209", "yWL210") ~ "YPH499"
),
receptor_p = case_when(
strain %in% c("yWL185", "yWL209") ~ "TIR1",
strain %in% c("yWL186", "yWL210") ~ "AFB2"
)
)#The time auxin addition is equal to time zero
time0 <-
"303112022-Pat-TCA03_Time-course assay_Auxin_yWL185-C1.fcs"
# or whatever well was being read when auxin was added
plate_03112022_sum$time <-
plate_03112022_sum$btime -
plate_03112022_sum[[which(plate_03112022_sum$name == time0), "btime"]]
#single bracket -->extracting the all name, 2 brackets extract just single "value" or "values"
#To normalize data
plate_03112022_sum <-
plate_03112022_sum %>% mutate(ratio = BL1.Amean/YL1.Amean) %>%
group_by(background_p, receptor_p) %>%
mutate(normalizedratio = ratio/mean(ratio))ratio <-
ggplot(data = subset(plate_03112022_sum),
aes(x = time, y = normalizedratio,
group = interaction(factor(colony), factor(treatment)),
linetype = factor(treatment), shape = factor(treatment),
color = factor(treatment))) +
geom_point(aes(color = treatment), size = 2) +
geom_line(aes(color = treatment), linewidth= 1) +
xlab("Time post auxin addition (min)") +
scale_shape_manual(values = c(19, 1)) +
ylab("Normalized Venus/mScarlet-I") +
facet_grid(background_p~receptor_p) +
scale_color_manual(values = c("#5499C7", "#999999")) +
theme_base() +
theme(legend.position = "none",
panel.grid.minor = element_line(linewidth = 0.3,
linetype = 'solid',
colour = "white"),
axis.line = element_line(colour = "black",
linewidth = 1, linetype = "solid"))
ratio
Without normalization
ratio_raw <-
ggplot(data = subset(plate_03112022_sum),
aes(x = time, y = ratio,
group = interaction(factor(colony), factor(treatment)),
linetype = factor(treatment), shape = factor(treatment),
color = factor(treatment))) +
geom_point(aes(color = treatment), size = 2) +
geom_line(aes(color = treatment), linewidth= 1) +
xlab("Time post auxin addition (min)") +
scale_shape_manual(values = c(19, 1)) +
ylab("Venus/mScarlet-I") +
facet_grid(background_p~receptor_p, scales = "free") +
scale_color_manual(values = c("#5499C7", "#999999")) +
theme_base() +
theme(legend.position = "none",
panel.grid.minor = element_line(linewidth = 0.3,
linetype = 'solid',
colour = "white"),
axis.line = element_line(colour = "black",
linewidth = 1, linetype = "solid"))
ratio_raw
ratio_raw / ratio + plot_annotation(tag_levels = "A") & theme(plot.background = element_blank())
ggsave("tc-strains.pdf", width = 5, height = 7)ggsave("tc-strains.png", width = 5, height = 7)
CV plot
Using the above time course dataset we can compare coefficients of variation in the individual parameters and the ratio for each of the strains and biosensors at steady state.
yWL185 (TIR1 dual-fusion, W303 yeast)
data185 <- steadyState(aplate_03112022, gated = TRUE)[1] "No further gating applied."
[1] "Converting events..."data185 <- subset(data185, strain == "yWL185" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL185-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL185-C1.fcs" ))
cv <- function(x) return(round(sd(x)/mean(x),2))
data185 <- subset(data185, BL1.A >1 & YL1.A > 1)
data185$FLratio <- data185$BL1.A/data185$YL1.A
range(data185$BL1.A)[1] 45 1048575cv <- function(x) return(round(sd(x)/mean(x),2))
data185$Venus <- data185$BL1.A / median(data185$BL1.A)
data185$mScarlet <- data185$YL1.A / median(data185$YL1.A)
data185$FLratio <- data185$BL1.A / data185$YL1.A
data185$ratio <- data185$FLratio / median(data185$FLratio)
data_long185 <- data185 %>%
dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
pivot_longer(cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value") %>%
dplyr::mutate(parameter = fct_relevel(parameter, "Venus"))
# need to also format CVs approriately for annotating
CV185 <-
data185 %>% group_by(treatment) %>%
dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
dplyr::select(treatment, Venus, mScarlet, ratio) %>%
pivot_longer(
cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value"
)
#data185
plot185 <- ggplot(data = data_long185,
mapping = aes(x = value,
color = treatment)) +
geom_density() + xlim(c(-1,4)) +
labs(x = "median normalized intensity", color = "treatment") + theme_test() +
geom_text(data = subset(CV185, treatment == "50 uM Auxin"),
aes(label = paste0("CV = ", value)),
x = 2, y = 1) +
geom_text(data = subset(CV185, treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0, y = 1) +
scale_color_viridis_d(option = "D", end = .75, direction = -1)
venus185 <- ggplot(data185, aes(x=data185$Venus, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5) + labs(x = "Venus", y ="Density") + xlim(-0.1,2)+ ylim(0,2) + theme_classic() + theme(legend.position="none") + geom_text(data = subset(CV185, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 1.2, y = 1.1) +
geom_text(data = subset(CV185, parameter == "Venus"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))
#venus185
red185 <- ggplot(data185, aes(x=data185$mScarlet, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5) + labs(x = "mScarlet", y ="Density") + xlim(-0.1,2) + ylim(0,2) + theme_classic() + theme(legend.position="none") + geom_text(data = subset(CV185, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 1.2, y = 1.1) +
geom_text(data = subset(CV185, parameter == "mScarlet"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))
#red185
ratio185<- ggplot(data185, aes(x=data185$ratio, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5) +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(-0.1,2) + ylim(0,3.5) + theme_classic() + theme(legend.position="none") + geom_text(data = subset(CV185, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 0.4, y = 2) +
geom_text(data = subset(CV185, parameter == "ratio"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 1.6, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))
#ratio185
plot185 <- grid.arrange(venus185, red185, ratio185, nrow=3, ncol=1)
plot185TableGrob (3 x 1) "arrange": 3 grobsyWL186 (AFB2 dual-fusion, W303 yeast)
data186 <- steadyState(aplate_03112022, gated = TRUE)[1] "No further gating applied."
[1] "Converting events..."data186 <- subset(data186, strain == "yWL186" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL186-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL186-C1.fcs" ))
cv <- function(x) return(round(sd(x)/mean(x),2))
data186 <- subset(data186, BL1.A >1 & YL1.A > 1)
data186$FLratio <- data186$BL1.A/data186$YL1.A
range(data186$BL1.A)[1] 25 1048575cv <- function(x) return(round(sd(x)/mean(x),2))
data186$Venus <- data186$BL1.A / median(data186$BL1.A)
data186$mScarlet <- data186$YL1.A / median(data186$YL1.A)
data186$FLratio <- data186$BL1.A / data186$YL1.A
data186$ratio <- data186$FLratio / median(data186$FLratio)
data_long186 <- data186 %>%
dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
pivot_longer(cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value") %>%
dplyr::mutate(parameter = fct_relevel(parameter, "Venus"))
# need to also format CVs approriately for annotating
CV186 <-
data186 %>% group_by(treatment) %>%
dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
dplyr::select(treatment, Venus, mScarlet, ratio) %>%
pivot_longer(
cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value"
)
#data186
plot186 <- ggplot(data = data_long186,
mapping = aes(x = value,
color = treatment)) +
geom_density() + xlim(c(-1,4)) +
labs(x = "median normalized intensity", color = "treatment") + theme_test() +
geom_text(data = subset(CV186, treatment == "50 uM Auxin"),
aes(label = paste0("CV = ", value)),
x = 2, y = 1) +
geom_text(data = subset(CV186, treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0, y = 1) +
scale_color_viridis_d(option = "D", end = .75, direction = -1)
venus186 <- ggplot(data186, aes(x=data186$Venus, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus (normalized median)", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() + theme(legend.position="none") + geom_text(data = subset(CV186, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 1.2, y = 1.1) +
geom_text(data = subset(CV186, parameter == "Venus"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))
red186 <- ggplot(data186, aes(x=data186$mScarlet, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet (normalized median)", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() + theme(legend.position="none") + scale_fill_manual(values=c("#EC7063", "#999999")) + geom_text(data = subset(CV186, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 1.2, y = 1.1) +
geom_text(data = subset(CV186, parameter == "mScarlet"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))
ratio186<- ggplot(data186, aes(x=data186$ratio, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() + theme(legend.position="none") + geom_text(data = subset(CV186, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 0.2, y = 2) +
geom_text(data = subset(CV186, parameter == "ratio"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 1.3, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))
plot186 <- grid.arrange(venus186, red186, ratio186, nrow=3, ncol=1)
plot186TableGrob (3 x 1) "arrange": 3 grobsyWL209 (TIR1 dual-fusion, YPH499 yeast)
data209 <- steadyState(aplate_03112022, gated = TRUE)[1] "No further gating applied."
[1] "Converting events..."data209 <- subset(data209, strain == "yWL209" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL209-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL209-C1.fcs" ))
cv <- function(x) return(round(sd(x)/mean(x),2))
data209 <- subset(data209, BL1.A >1 & YL1.A > 1)
data209$FLratio <- data209$BL1.A/data209$YL1.A
range(data209$BL1.A)[1] 9 1048575cv <- function(x) return(round(sd(x)/mean(x),2))
data209$Venus <- data209$BL1.A / median(data209$BL1.A)
data209$mScarlet <- data209$YL1.A / median(data209$YL1.A)
data209$FLratio <- data209$BL1.A / data209$YL1.A
data209$ratio <- data209$FLratio / median(data209$FLratio)
data_long209 <- data209 %>%
dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
pivot_longer(cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value") %>%
dplyr::mutate(parameter = fct_relevel(parameter, "Venus"))
# need to also format CVs approriately for annotating
CV209 <-
data209 %>% group_by(treatment) %>%
dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
dplyr::select(treatment, Venus, mScarlet, ratio) %>%
pivot_longer(
cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value"
)
#data209
plot209 <- ggplot(data = data_long209,
mapping = aes(x = value,
color = treatment)) +
geom_density() + xlim(c(-1,4)) +
labs(x = "median normalized intensity", color = "treatment") + theme_test() +
geom_text(data = subset(CV209, treatment == "50 uM Auxin"),
aes(label = paste0("CV = ", value)),
x = 1.2, y = 1) +
geom_text(data = subset(CV209, treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0, y = 1) +
scale_color_viridis_d(option = "D", end = .75, direction = -1)
venus209 <- ggplot(data209, aes(x=data209$Venus, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() + theme(legend.position="none") + geom_text(data = subset(CV209, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 1.2, y = 1.1) +
geom_text(data = subset(CV209, parameter == "Venus"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))
red209 <- ggplot(data209, aes(x=data209$mScarlet, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() + theme(legend.position="none") + geom_text(data = subset(CV209, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 1.2, y = 1.1) +
geom_text(data = subset(CV209, parameter == "mScarlet"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))
ratio209<- ggplot(data209, aes(x=data209$ratio, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() + theme(legend.position="none") + geom_text(data = subset(CV209, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 0.3, y = 2) +
geom_text(data = subset(CV209, parameter == "ratio"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 1.6, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))
plot209 <- grid.arrange(venus209, red209, ratio209, nrow=3, ncol=1)
plot209TableGrob (3 x 1) "arrange": 3 grobsyWL210 (AFB2 dual-fusion, YPH499 yeast)
data210 <- steadyState(aplate_03112022, gated = TRUE)[1] "No further gating applied."
[1] "Converting events..."#data <- tidyFlow(aplate_20210619_W303)
data210 <- subset(data210, strain == "yWL210" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL210-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL210-C1.fcs" ))
cv <- function(x) return(round(sd(x)/mean(x),2))
data210 <- subset(data210, BL1.A >1 & YL1.A > 1)
data210$FLratio <- data210$BL1.A/data210$YL1.A
range(data210$BL1.A)[1] 25 1048575cv <- function(x) return(round(sd(x)/mean(x),2))
data210$Venus <- data210$BL1.A / median(data210$BL1.A)
data210$mScarlet <- data210$YL1.A / median(data210$YL1.A)
data210$FLratio <- data210$BL1.A / data210$YL1.A
data210$ratio <- data210$FLratio / median(data210$FLratio)
data_long210 <- data210 %>%
dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
pivot_longer(cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value") %>%
dplyr::mutate(parameter = fct_relevel(parameter, "Venus"))
# need to also format CVs approriately for annotating
CV210 <-
data210 %>% group_by(treatment) %>%
dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
dplyr::select(treatment, Venus, mScarlet, ratio) %>%
pivot_longer(
cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value"
)
#data210
plot210 <- ggplot(data = data_long210,
mapping = aes(x = value,
color = treatment)) +
geom_density() + xlim(c(-1,4)) +
labs(x = "median normalized intensity", color = "treatment") + theme_test() +
geom_text(data = subset(CV210, treatment == "50 uM Auxin"),
aes(label = paste0("CV = ", value)),
x = 2, y = 1) +
geom_text(data = subset(CV210, treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0, y = 1) +
scale_color_viridis_d(option = "D", end = .75, direction = -1) +
facet_grid(parameter~.)
venus210 <- ggplot(data210, aes(x=data210$Venus, group=treatment,color=treatment, fill=treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() + theme(legend.position="none") + geom_text(data = subset(CV210, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 1.5, y = 1) +
geom_text(data = subset(CV210, parameter == "Venus"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0.7, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))
red210 <- ggplot(data210, aes(x=data210$mScarlet, group=treatment,color=treatment, fill=treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() + theme(legend.position="none") + geom_text(data = subset(CV210, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 1.2, y = 1.2) +
geom_text(data = subset(CV210, parameter == "mScarlet"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 0.4, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))
ratio210<- ggplot(data210, aes(x=data210$ratio, group=treatment, color=treatment, fill = treatment)) + geom_density(adjust=1.5, alpha=.4) +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() + theme(legend.position="none") + geom_text(data = subset(CV210, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
x = 0.2, y = 1.8) +
geom_text(data = subset(CV210, parameter == "ratio"& treatment == "Control"),
aes(label = paste0("CV = ", value)),
x = 1.6, y = 1.8) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))
#ratio210
plot210 <- grid.arrange(venus210, red210, ratio210, nrow=3, ncol=1)
plot210TableGrob (3 x 1) "arrange": 3 grobsdual_fusion_cv <- (venus210 + ggtitle("AFB2")+
theme(plot.title = element_text(hjust = 0.5)) |
venus209 + ggtitle("TIR1") +
theme(plot.title = element_text(hjust = 0.5))) /
(red210 | red209) /
(ratio210 | ratio209) +
theme(title = element_text(hjust = 0))
dual_fusion_cv
ggsave("dual_fusion_cv.pdf", width = 6.3, height = 6)ggsave("dual_fusion_cv.png", width = 6.3, height = 6)
Single-fusion vs dual-fusion comparison in W303 yeast strain
- yWL161 (TIR1 single-fusion, W303 yeast)
- yWL162 (AFB2 single-fusion, W303 yeast)
- yWL185 (TIR1 dual-fusion, W303 yeast)
- yWL186 (AFB2 dual-fusion, W303 yeast)
plate_20210619_W303 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/W303only/", pattern = "r*") annotation <- createAnnotation(yourFlowSet = plate_20210619_W303)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_annotation.csv')annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_annotation.csv')
aplate_20210619_W303 <- annotateFlowSet(yourFlowSet = plate_20210619_W303, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_20210619_W303)))
head(pData(aplate_20210619_W303))
write.flowSet(aplate_20210619_W303, outdir = "flowSets/design-relative-expression")aplate_20210619_W303 <- read.flowSet(path = "flowSets/design-relative-expression/", phenoData = "annotation.txt")
plate_20210619_W303_sum <- summarizeFlow(aplate_20210619_W303, gated = TRUE)[1] "Summarizing all events..."#The time auxin addition is equal to time zero
time0 <- "4E01.fcs"
# or whatever well was being read when auxin was added
plate_20210619_W303_sum$time <- plate_20210619_W303_sum$btime-plate_20210619_W303_sum[[which(plate_20210619_W303_sum$name == time0), "btime"]]
#single bracket -->extracting the all name, 2 brackets extract just single "value" or "values"
plate_20210619_W303_sum <- plate_20210619_W303_sum %>%
mutate(
design = case_when(
strain %in% c("yWL185","yWL186") ~ "dual-fusion",
strain %in% c("yWL161","yWL162") ~ "single-fusion"),
fbox = case_when(
strain %in% c("yWL161","yWL185") ~ "TIR1",
strain %in% c("yWL162","yWL186") ~ "AFB2")) %>%
mutate(design_construct = paste(fbox, design))
plate_20210619_W303_sum <-
plate_20210619_W303_sum %>% mutate(ratio = FL1.Amean/FL4.Amean) %>%
group_by(design, fbox) %>%
mutate(normalizedratio = ratio/mean(ratio))
plate_20210619_W303_sum<-
plate_20210619_W303_sum %>% mutate(green = FL1.Amean) %>%
group_by(design, fbox) %>%
mutate(normalized_Greenexpression = FL1.Amean/mean(FL1.Amean))
plate_20210619_W303_sum <-
plate_20210619_W303_sum %>% mutate(red = FL4.Amean) %>%
group_by(design, fbox) %>%
mutate(normalized_Redexpression = FL4.Amean/mean(FL4.Amean))TIR1_single <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL161"), group = factor(treatment), linetype = factor(treatment), shape = factor(treatment), color = factor(treatment)) +
xlab("Time (min)") +
geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5) + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y")
AFB2_single <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL162"), group = factor(treatment), linetype = factor(treatment), shape = factor(treatment), color = factor(treatment)) +
xlab("Time (min)") +
geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5) + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y")
TIR1_dual <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL185"), group = factor(treatment), linetype = factor(treatment), shape = factor(treatment), color = factor(treatment)) +
geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5) + xlab("Time (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y")
AFB2_dual <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL186"), group = factor(treatment), linetype = factor(treatment), shape = factor(treatment), color = factor(treatment)) +
xlab("Time (min)") +
geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5) + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y")
grid.arrange(AFB2_single, TIR1_single, TIR1_dual, AFB2_dual, nrow=2, ncol=4)
W303ratio <- qplot(x = time, y = normalizedratio, data = subset(plate_20210619_W303_sum), group = factor(treatment), linetype = factor(treatment), shape = factor(treatment), color = factor(treatment)) + xlab("Time (min)") +
geom_point(aes(color = treatment), size = 0.5) + geom_line(aes(color = treatment), linewidth = 1) + scale_shape_manual(values = c(19, 1)) + ylab("Venus") + scale_color_manual(values = c("#2E86C1", "#626567")) + theme_minimal() + theme(legend.position = "bottom", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black",
linewidth = 1, linetype = "solid")) + facet_wrap(~design_construct, scale ="free_y")
W303venus <- qplot(x = time, y = normalized_Greenexpression, data = subset(plate_20210619_W303_sum), group = factor(treatment), linetype = factor(treatment), shape = factor(treatment), color = factor(treatment)) + xlab("Time (min)") +
geom_point(aes(color = treatment), size = 0.5) + geom_line(aes(color = treatment), linewidth = 1) + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet") + scale_color_manual(values = c("#F1C40F", "#626567")) + theme_minimal() + theme(legend.position = "bottom", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black",
linewidth = 1, linetype = "solid")) + facet_wrap(~design_construct, scale ="free_y")
grid.arrange(W303venus, W303ratio, nrow=2, ncol=2)
Venus_aov <- aov(FL1.Amean ~ design_construct * treatment * before_after,
data = plate_20210619_W303_sum %>%
dplyr::filter(time <= 0 | time >= 200))
summary(Venus_aov) Df Sum Sq Mean Sq F value Pr(>F)
design_construct 3 5.902e+09 1.967e+09 108.760 < 2e-16 ***
treatment 1 9.695e+08 9.695e+08 53.601 5.82e-09 ***
before_after 1 4.570e+06 4.570e+06 0.253 0.618
design_construct:treatment 3 5.293e+07 1.764e+07 0.976 0.414
design_construct:before_after 3 9.180e+07 3.060e+07 1.692 0.184
treatment:before_after 1 6.641e+08 6.641e+08 36.715 3.56e-07 ***
design_construct:treatment:before_after 3 3.063e+07 1.021e+07 0.564 0.642
Residuals 41 7.416e+08 1.809e+07
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1(Venus_HSD.test <-
agricolae::HSD.test(Venus_aov,
trt = c("design_construct",
"treatment",
"before_after"),
console = TRUE))
Study: Venus_aov ~ c("design_construct", "treatment", "before_after")
HSD Test for FL1.Amean
Mean Square Error: 18087298
design_construct:treatment:before_after, means
Alpha: 0.05 ; DF Error: 41
Critical Value of Studentized Range: 5.154955
Groups according to probability of means differences and alpha level( 0.05 )
Treatments with the same letter are not significantly different.$statistics
MSerror Df Mean CV
18087298 41 49475.37 8.596028
$parameters
test name.t ntr StudentizedRange alpha
Tukey design_construct:treatment:before_after 16 5.154955 0.05
$means
FL1.Amean std r Min Max Q25 Q50 Q75
AFB2 dual-fusion:Auxin:after 24425.97 3887.943 4 19046.09 27855.18 22934.16 25401.31 26893.13
AFB2 dual-fusion:Auxin:before 34631.51 2164.181 3 33214.07 37122.60 33385.97 33557.87 35340.23
AFB2 dual-fusion:Control EtOH:after 43943.78 2563.443 4 40314.31 46305.36 43299.85 44577.73 45221.67
AFB2 dual-fusion:Control EtOH:before 35281.28 747.563 3 34785.81 36141.17 34851.33 34916.85 35529.01
AFB2 single-fusion:Auxin:after 48152.32 8233.437 4 41566.06 58870.26 41748.87 46086.48 52489.94
AFB2 single-fusion:Auxin:before 52801.44 3789.368 3 49120.11 56690.32 50856.99 52593.87 54642.10
AFB2 single-fusion:Control EtOH:after 61495.06 1241.776 4 59958.17 62859.15 60845.16 61581.46 62231.36
AFB2 single-fusion:Control EtOH:before 53911.90 2286.620 3 51418.04 55909.91 52912.90 54407.76 55158.83
TIR1 dual-fusion:Auxin:after 41225.93 2818.571 4 37362.85 43733.63 40076.43 41903.61 43053.11
TIR1 dual-fusion:Auxin:before 46003.40 1595.801 3 44645.69 47761.17 45124.52 45603.34 46682.25
TIR1 dual-fusion:Control EtOH:after 53108.52 5964.120 4 46969.08 60966.52 49652.60 52249.25 55705.17
TIR1 dual-fusion:Control EtOH:before 44671.84 1768.004 3 43508.04 46706.33 43654.60 43801.16 45253.75
TIR1 single-fusion:Auxin:after 53550.99 3388.730 4 50475.84 58381.61 51941.54 52673.26 54282.71
TIR1 single-fusion:Auxin:before 63962.75 3201.204 3 61073.09 67403.85 62242.20 63411.32 65407.59
TIR1 single-fusion:Control EtOH:after 65619.10 7089.748 5 53646.79 71126.31 64830.36 68765.73 69726.29
TIR1 single-fusion:Control EtOH:before 64865.95 3237.883 3 61178.65 67245.10 63676.38 66174.10 66709.60
$comparison
NULL
$groups
FL1.Amean groups
TIR1 single-fusion:Control EtOH:after 65619.10 a
TIR1 single-fusion:Control EtOH:before 64865.95 ab
TIR1 single-fusion:Auxin:before 63962.75 ab
AFB2 single-fusion:Control EtOH:after 61495.06 ab
AFB2 single-fusion:Control EtOH:before 53911.90 bc
TIR1 single-fusion:Auxin:after 53550.99 bc
TIR1 dual-fusion:Control EtOH:after 53108.52 bc
AFB2 single-fusion:Auxin:before 52801.44 bcd
AFB2 single-fusion:Auxin:after 48152.32 cd
TIR1 dual-fusion:Auxin:before 46003.40 cde
TIR1 dual-fusion:Control EtOH:before 44671.84 cde
AFB2 dual-fusion:Control EtOH:after 43943.78 cde
TIR1 dual-fusion:Auxin:after 41225.93 de
AFB2 dual-fusion:Control EtOH:before 35281.28 ef
AFB2 dual-fusion:Auxin:before 34631.51 ef
AFB2 dual-fusion:Auxin:after 24425.97 f
attr(,"class")
[1] "group"Venus_groups <- Venus_HSD.test$groups %>%
as_tibble(rownames = "names") %>%
separate(col = names, into = c("design_construct",
"treatment",
"before_after"),
sep = "\\:", remove = FALSE) %>%
left_join(Venus_HSD.test$means %>%
as_tibble(rownames = "names") %>%
dplyr::select(c(names, Max)),
by = "names") %>%
mutate(treatment = fct_rev(treatment),
before_after = fct_rev(before_after))
mScarlet_aov <- aov(FL4.Amean ~ design_construct * treatment * before_after,
data = plate_20210619_W303_sum %>%
dplyr::filter(time <= 0 | time >= 200))
summary(mScarlet_aov) Df Sum Sq Mean Sq F value Pr(>F)
design_construct 3 1.318e+10 4.393e+09 1152.935 < 2e-16 ***
treatment 1 2.843e+06 2.843e+06 0.746 0.39279
before_after 1 7.962e+07 7.962e+07 20.894 4.41e-05 ***
design_construct:treatment 3 4.175e+06 1.392e+06 0.365 0.77844
design_construct:before_after 3 6.634e+07 2.211e+07 5.803 0.00211 **
treatment:before_after 1 7.820e+02 7.820e+02 0.000 0.98864
design_construct:treatment:before_after 3 7.087e+06 2.362e+06 0.620 0.60610
Residuals 41 1.562e+08 3.811e+06
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1(mScarlet_HSD.test <-
agricolae::HSD.test(mScarlet_aov,
trt = c("design_construct",
"treatment",
"before_after"),
console = TRUE))
Study: mScarlet_aov ~ c("design_construct", "treatment", "before_after")
HSD Test for FL4.Amean
Mean Square Error: 3810672
design_construct:treatment:before_after, means
Alpha: 0.05 ; DF Error: 41
Critical Value of Studentized Range: 5.154955
Groups according to probability of means differences and alpha level( 0.05 )
Treatments with the same letter are not significantly different.$statistics
MSerror Df Mean CV
3810672 41 19226.9 10.15293
$parameters
test name.t ntr StudentizedRange alpha
Tukey design_construct:treatment:before_after 16 5.154955 0.05
$means
FL4.Amean std r Min Max Q25 Q50
AFB2 dual-fusion:Auxin:after 2490.844 198.85812 4 2207.416 2666.530 2438.961 2544.714
AFB2 dual-fusion:Auxin:before 2026.397 46.66275 3 1973.433 2061.453 2008.869 2044.305
AFB2 dual-fusion:Control EtOH:after 2759.117 144.66745 4 2612.374 2929.881 2655.805 2747.106
AFB2 dual-fusion:Control EtOH:before 2110.115 112.55555 3 2013.715 2233.807 2048.269 2082.823
AFB2 single-fusion:Auxin:after 36197.490 5828.74906 4 31851.266 44192.014 31870.387 34373.340
AFB2 single-fusion:Auxin:before 31074.988 1904.65789 3 29016.085 32774.074 30225.445 31434.806
AFB2 single-fusion:Control EtOH:after 38385.588 1153.01803 4 36886.238 39681.451 37980.543 38487.331
AFB2 single-fusion:Control EtOH:before 31251.821 936.56398 3 30193.587 31973.960 30890.752 31587.918
TIR1 dual-fusion:Auxin:after 5753.411 381.70386 4 5202.843 6084.835 5692.279 5862.984
TIR1 dual-fusion:Auxin:before 4582.236 107.55234 3 4483.615 4696.914 4524.898 4566.180
TIR1 dual-fusion:Control EtOH:after 5556.851 452.28202 4 5075.024 6086.477 5250.871 5532.952
TIR1 dual-fusion:Control EtOH:before 4557.292 144.44763 3 4390.506 4642.111 4514.882 4639.258
TIR1 single-fusion:Auxin:after 34660.947 2185.28924 4 33220.282 37917.098 33608.882 33753.203
TIR1 single-fusion:Auxin:before 31775.292 1187.70866 3 30675.467 33034.740 31145.568 31615.670
TIR1 single-fusion:Control EtOH:after 34156.499 2380.95848 5 30640.164 37060.388 33524.915 34184.394
TIR1 single-fusion:Control EtOH:before 33266.440 288.20961 3 32971.733 33547.680 33125.820 33279.906
Q75
AFB2 dual-fusion:Auxin:after 2596.597
AFB2 dual-fusion:Auxin:before 2052.879
AFB2 dual-fusion:Control EtOH:after 2850.417
AFB2 dual-fusion:Control EtOH:before 2158.315
AFB2 single-fusion:Auxin:after 38700.443
AFB2 single-fusion:Auxin:before 32104.440
AFB2 single-fusion:Control EtOH:after 38892.376
AFB2 single-fusion:Control EtOH:before 31780.939
TIR1 dual-fusion:Auxin:after 5924.116
TIR1 dual-fusion:Auxin:before 4631.547
TIR1 dual-fusion:Control EtOH:after 5838.933
TIR1 dual-fusion:Control EtOH:before 4640.685
TIR1 single-fusion:Auxin:after 34805.267
TIR1 single-fusion:Auxin:before 32325.205
TIR1 single-fusion:Control EtOH:after 35372.637
TIR1 single-fusion:Control EtOH:before 33413.793
$comparison
NULL
$groups
FL4.Amean groups
AFB2 single-fusion:Control EtOH:after 38385.588 a
AFB2 single-fusion:Auxin:after 36197.490 ab
TIR1 single-fusion:Auxin:after 34660.947 ab
TIR1 single-fusion:Control EtOH:after 34156.499 ab
TIR1 single-fusion:Control EtOH:before 33266.440 ab
TIR1 single-fusion:Auxin:before 31775.292 b
AFB2 single-fusion:Control EtOH:before 31251.821 b
AFB2 single-fusion:Auxin:before 31074.988 b
TIR1 dual-fusion:Auxin:after 5753.411 c
TIR1 dual-fusion:Control EtOH:after 5556.851 c
TIR1 dual-fusion:Auxin:before 4582.236 c
TIR1 dual-fusion:Control EtOH:before 4557.292 c
AFB2 dual-fusion:Control EtOH:after 2759.117 c
AFB2 dual-fusion:Auxin:after 2490.844 c
AFB2 dual-fusion:Control EtOH:before 2110.115 c
AFB2 dual-fusion:Auxin:before 2026.397 c
attr(,"class")
[1] "group"mScarlet_groups <- mScarlet_HSD.test$groups %>%
as_tibble(rownames = "names") %>%
separate(col = names, into = c("design_construct",
"treatment",
"before_after"),
sep = "\\:", remove = FALSE) %>%
left_join(mScarlet_HSD.test$means %>%
as_tibble(rownames = "names") %>%
dplyr::select(c(names, Max)),
by = "names") %>%
mutate(treatment = fct_rev(treatment),
before_after = fct_rev(before_after))
boxvenus <-
plate_20210619_W303_sum %>%
dplyr::filter(time <= 0 | time >= 200) %>%
ggplot(aes(
x = fct_rev(before_after),
y = FL1.Amean/1000,
)) +
geom_boxplot(
aes(fill = fct_rev(treatment)), alpha = 0.7,
outlier.shape = NA, show.legend = FALSE) +
geom_point(aes(color = fct_rev(treatment)),
position = position_dodge2(width = .55),
size = 1) +
geom_text(data = Venus_groups,
mapping = aes(y = 1.05*Max/1000, label = groups),
position = position_dodge2(width = 1),
color = "black") +
scale_fill_manual(values = c("#8f8f8f", "#edc215")) +
scale_color_manual(values = c("#8f8f8f", "#edc215")) +
ylab("Venus") +
xlab("50 µM Auxin treatment") +
facet_wrap( ~ fbox, scale = "free_y") +
facet_grid( ~ design_construct) +
theme_classic() + labs(color = "")
boxmScarlet <-
plate_20210619_W303_sum %>%
dplyr::filter(time <= 0 | time >= 200) %>%
ggplot(aes(
x = fct_rev(before_after),
y = FL4.Amean/1000
)) +
geom_boxplot(aes(fill = fct_rev(treatment)), alpha = .7,
outlier.shape = NA, show.legend = FALSE) +
geom_point(aes(color = fct_rev(treatment)),
position = position_dodge2(width = .55),
size = 1) +
geom_text(data = mScarlet_groups,
mapping = aes(y = 1.2*Max/1000, label = groups),
position = position_dodge2(width = 1),
color = "black") +
scale_fill_manual(values = c("#8f8f8f", "#EC7063")) +
scale_color_manual(values = c("#8f8f8f", "#EC7063")) +
ylab("mScarlet-I") +
xlab("50 µM Auxin treatment") +
facet_wrap( ~ fbox, scale = "free_y") +
facet_grid( ~ design_construct, scales = "free_y") +
theme_classic() + labs(color = "") + scale_y_log10()
guide_area() / boxvenus / boxmScarlet +
plot_annotation(tag_levels = "A") +
plot_layout(guides = "collect", heights = c(.3, 1,1)) &
theme(legend.box = "horizontal",
legend.spacing = unit(0, "pt"),
legend.justification = "top",
legend.margin = margin(),
legend.box.spacing = unit(0, "pt"),
legend.background = element_blank(),
legend.box.background = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
ggsave("relative-expression-box.pdf", width = 6, height = 5)ggsave("relative-expression-box.png", width = 6, height = 5)
plate_20210619_read3and12 <- read.flowSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/W303only_read3and12/", alter.names = TRUE) annotation <- createAnnotation(yourFlowSet = plate_20210619_read3and12)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_read3and12_annotation.csv')annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_read3and12_annotation.csv')
aplate_20210619_read3and12<- annotateFlowSet(yourFlowSet = plate_20210619_read3and12, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_20210619_read3and12)))[1] "D01.fcs" "D02.fcs" "D03.fcs" "D04.fcs" "D07.fcs" "D08.fcs"head(pData(aplate_20210619_read3and12))NAW303_read3and12 <- summarizeFlow(aplate_20210619_read3and12, gated = TRUE)[1] "Summarizing all events..."W303_read3and12 <- W303_read3and12 %>%
mutate(design = str_extract(design_construct, "(?<=\\s).*")) %>%
mutate(design = str_extract(design_construct, ".*(?=\\s)"))
venus3and12_unnorm <-
ggplot(W303_read3and12,
aes(x=design_construct,
y=FL1.Amean,
alpha = fct_rev(before_after),
group=treatment)) +
geom_point(aes(colour = factor(treatment),
shape = factor(treatment)), size = 2,
position = position_jitterdodge(
dodge.width = 0.7,
jitter.width = 0.5)) +
scale_color_manual(values=c("#F1C40F", "#5F6A6A")) +
ylab("Venus") + theme_classic() +
theme(axis.text.x = element_text(angle=45, hjust = 1)) +
scale_alpha_manual(values = c(0.3, 0.8))
mScarlet3and12_unnorm <-
ggplot(W303_read3and12,
aes(x=design_construct,
y=FL4.Amean,
alpha = fct_rev(before_after),
group=treatment)) +
geom_point(aes(colour = factor(treatment),
shape = factor(treatment)),
size = 2,
position = position_jitterdodge(
dodge.width = .7,
jitter.width =.5)) +
scale_color_manual(values=c("#E74C3C", "#5F6A6A")) +
ylab("mScarlet-I") + theme_classic() +
theme(axis.text.x = element_text(angle=45, hjust = 1)) +
scale_alpha_manual(values = c(0.3, 0.8))
guide_area() / (venus3and12_unnorm | mScarlet3and12_unnorm) +
plot_layout(guides = "collect",
heights = c(1, 3)) +
plot_annotation(tag_levels = "A") &
theme(legend.position='top', legend.direction = "vertical",
legend.title = element_blank(),
axis.title.x = element_blank(),
legend.justification = "left",
legend.box.just = "left",
legend.margin = margin())
ggsave("relative-expression.png", width = 4, height = 3)ggsave("relative-expression.pdf", width = 4, height = 3)
Coefficient of variation (CV) analysis
data <- steadyState(aplate_20210619_W303, gated = TRUE)[1] "No further gating applied."
[1] "Converting events..."#data <- tidyFlow(aplate_20210619_W303)
data <- subset(data, strain == "yWL161" & name %in% c("11L01.fcs", "11L07.fcs"))
#range(data$FL1.A)
#sd(data$FLratio)/mean(data$FLratio)
#range(data$FLratio)
data <- subset(data, FL1.A >1 & FL4.A > 1)
data$FLratio <- data$FL1.A/data$FL4.A
range(data$FL1.A)[1] 722 2579491#calculate cvs
sd(data$FLratio)/mean(data$FLratio)[1] 1.821223range(data$FLratio)[1] 0.06052985 488.44186047cv <- function(x) return(round(sd(x)/mean(x),2))
#calculate normalized values
data$Venus <- data$FL1.A / median(data$FL1.A)
data$mScarlet <- data$FL4.A / median(data$FL4.A)
data$ratio <- data$FLratio / median(data$FLratio)
CVs <- data %>% group_by(treatment) %>%
summarise(across(where(is_double), cv))
# make a tidy, long dataset
data_long <- data %>%
dplyr::select(treatment, Venus, mScarlet, ratio) %>%
pivot_longer(cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value")
# need to also format CVs appropriately for annotating
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
pivot_longer(cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value")
#data
CV_plot <- ggplot(data = data_long,
mapping = aes(x = value,
color = treatment)) +
geom_density() + xlim(c(-1,4)) +
labs(x = "median normalized intensity", color = "treatment") +
facet_grid(parameter~.) + theme_test() +
geom_text(data = subset(CVs, treatment == "Auxin"),
aes(label = paste0("CV = ", value)),
x = 0.1, y = 0.6) +
geom_text(data = subset(CVs, treatment == "Control EtOH"),
aes(label = paste0("CV = ", value)),
x = 1.8, y = 0.6) +
scale_color_viridis_d(option = "D", end = .75, direction = -1)
CV_plot
Auxin prodcution in stationary phase
Read in annotated flowSet
flowSet <- read.flowSet(path = paste0("~/Google Drive/Shared drives/PlantSynBioLab/Auxin Biosensor Manuscript/Auxin Biosensor Data/FlowSets/", experiment_date, "_", experiment_name), phenoData = "annotation.txt")
write.flowSet(flowSet, "flowSets/auxin-biosynthesis")flowSet <- read.flowSet(path = "flowSets/auxin-biosynthesis/", phenoData = "annotation.txt")Summary Analysis
load("PSB_Accuri_W303.RData")
dat_sum <- summarizeFlow(flowSet, ploidy = "haploid", only = "yeast") # These gates might not work well for YPH499, but there was a lot of debris[1] "Gating with haploid yeast gate..."
[1] "Summarizing all yeast events..."dat_sum <- mutate(.data = dat_sum, across(starts_with("FL"), as.numeric)) %>%
mutate(strain = str_remove(strain, " ratiometric sensor") %>%
str_replace(pattern = "in trans",
replacement = "single-fusion") %>%
str_replace(pattern = "in cis",
replacement = "dual-fusion"))
dat_sum$pred_auxin <- dat_sum$FL4.Amean/dat_sum$FL1.Amean
dat_sum$pred_auxin_SE <- with(dat_sum,
sqrt((FL4.Asd/100/FL4.Amean)^2 +
(FL1.Asd/100/FL1.Amean)^2)*pred_auxin)
dat_sum$pred_auxin_sd <- with(dat_sum,
sqrt((FL4.Asd/FL4.Amean)^2 +
(FL1.Asd/FL1.Amean)^2)*pred_auxin)ggplot(data = dat_sum,
aes(x = fct_reorder(treat, pred_auxin), y = pred_auxin,
color = factor(strain))) +
geom_point() +
theme(axis.text.x = element_text(angle = 45,
hjust = 1, vjust = 1)) +
facet_grid(.~strain, scales = "free_x") + coord_flip() +
theme(legend.position = "bottom",
legend.direction = "vertical") +
labs(x = "", y = "Predicted auxin (mScarlet-I/Venus-IAA17)",
color = "strain")
ggsave("strain_sensor_comparison_raw.pdf", width = 8, height = 3)
dat_sum <- dat_sum %>% group_by(strain) %>%
mutate(norm_pred_auxin =
pred_auxin / mean(pred_auxin
[which(.data$treat ==
"aerobic exponential phase")]))
ggplot(data = dat_sum, aes(x = fct_reorder(treat, norm_pred_auxin),
y = norm_pred_auxin,
color = factor(strain))) +
geom_point(position = position_jitter(width = 0.2)) +
theme(axis.text.x = element_text(angle = 45,
hjust = 1, vjust = 1)) +
coord_flip() + theme(legend.position = "top",
legend.direction = "vertical") +
labs(x = "",
y = "Normalized Predicted auxin (mScarlet-I/Venus-IAA17)",
color = "strain")
ggsave("strain_sensor_comparison_norm.pdf", width = 5, height = 5)
Full distributions
data <- steadyState(flowset = flowSet, ploidy = "haploid", only = "yeast")[1] "Gating with haploid yeast gate..."
[1] "Converting events..."hist(x = log(data$FL1.A, 10))
hist(x = log(data$FL4.A, 10))
data$pred_auxin <- data$FL4.A/data$FL1.A
data <- data %>% dplyr::filter(FL1.A > 1 & FL4.A > 1)
data$treat <- data$treat %>% str_remove("phase")endog_auxin <- ggplot(
data = subset(
data,
strain == "W303 ratiometric sensor AFB2 in cis" &
pred_auxin < 2),
aes(
x = pred_auxin,
y = fct_reorder(treat, .x = pred_auxin,
.fun = median, .desc = TRUE) %>%
fct_relevel("aerobic exponential ", after = Inf),
fill = factor(stat(quantile))
)
) +
stat_density_ridges(
geom = "density_ridges_gradient",
calc_ecdf = TRUE,
quantiles = 4,
color = "grey",
alpha = 0.5
) +
scale_fill_viridis_d(name = "Quartiles") +
theme(legend.position = NULL) +
#facet_wrap(.~treat) +
xlim(c(-0.1, 1)) +
labs(y = NULL, x = "Predicted relative auxin production (AU) \n (AFB2-mScarlet-I/Venus-IAA17)") + theme_ridges() + theme(legend.position = "none")
ggsave("20210626_auxin_production_quartiles.pdf", width = 5, height = 4)
endog_auxin
Biosensor sensitivity at stationary phase
plate_09282022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/09282022_yWL210_DRA-stationary/Data/", pattern = "S-DRA*") annotation_09282022 <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/09282022_yWL210_DRA-stationary/09282022_stationaryphase_annotation.csv')
aplate_09282022 <- annotateFlowSet(yourFlowSet = plate_09282022, annotation_df = annotation_09282022, mergeBy = 'name')
head(rownames(pData(aplate_09282022)))
head(pData(aplate_09282022))
write.flowSet(aplate_09282022, "flowSets/AFB2-dual-stationary")aplate_09282022 <- read.flowSet(path = "flowSets/AFB2-dual-stationary/", phenoData = "annotation.txt")
dat_sumGr_09282022 <- summarizeFlow(aplate_09282022, gated = TRUE)[1] "Summarizing all events..."model.LL4_09282022<- drm(YL1.Amean/BL1.Amean~dose, data=subset(dat_sumGr_09282022, set == "4"), fct=LL.4(names = c("Slope", "Lower Limit", "Upper Limit", "ED50")))
pm210stat <- expand.grid(treatment=exp(seq(log(1e-5), log(1e2), length=1000)))
pm210stat <- cbind(pm210stat,predict(model.LL4_09282022, newdata=pm210stat, interval="confidence")) #m2all = model of 210 data
plot210_stat <- ggplot(subset(dat_sumGr_09282022, set == "4"),
aes(x = dose, y = YL1.Amean/BL1.Amean)) +
geom_ribbon(data = pm210stat,
aes(x = treatment, y = Prediction,
ymin = Lower, ymax=Upper), alpha = .2) +
geom_line(data = pm210stat, aes(x = treatment, y = Prediction)) +
ylab("AFB2-mScarlet-I/Venus-IAA17") +
xlab("Exogenous Auxin (µM)") +
scale_x_log10(labels =
scales::label_number(drop0trailing = TRUE)) +
scale_color_viridis_d() + geom_point() +
theme_classic(base_size = 14) +
theme(legend.position = "none")
plot210_stat
Combined figure
endog_auxin + plot210_stat + plot_annotation(tag_levels = "A")
ggsave("auxin-accumulation.pdf", width = 8, height = 4)
ggsave("auxin-accumulation.png", width = 8, height = 4)Mutant library analysis
Here we aim to test auxin induced Venus-IAA17 degradation relative to the bicistronic mScarlet-I control with AFB2 expressed from the p1 plasmid in the OrthoRep continuous mutagenesis system.
Procedure
Temperature 30 degree celcius Shaking at 250rpm DO-URA-HIS, starting volume: 10 mL Overnight concentration: 30 events/uL 1PM started from 2 colonies
Initial reading ~10AM Auxin concentration: 100 uM (.1% DMSO) After 4th reading: B04.fcs
###Importing and annotating data
aplate1 <- read.flowSet(path = 'flowSets/OrthoRep', phenoData = "annotation.txt")
dat_sum <- summarizeFlow(aplate1, gated = TRUE)[1] "Summarizing all events..."data <- flowTime::tidyFlow(aplate1, gated = TRUE)[1] "No further gating applied."
[1] "Converting events..."data <- subset(data, FL4.A > 1)
range(data$FL1.A)[1] 0 15867629range(data$FL4.A)[1] 6 7690137data$ratio <- data$FL1.A / data$FL4.A
data <- subset(data, data$ratio < 10)Make a kernel density plot overlapping mutant population with parent, DMSO and IAA that we can then stack a few timepoints with facets. For timepoints it looks like the 2nd, 7th and 12th would be a good demonstration of the timecourse.
wells <- dat_sum %>%
# Get well/file names of the 2nd, 7th and 12th readings of the 4 strains.
dplyr::slice(1:4 + unlist(lapply((c(2, 7, 12) - 1)*4, rep, 4))) %>%
dplyr::pull("name")
data <-
dplyr::filter(data, name %in% wells)
data$approxtime <- signif(data$etime, digits = 1)
signif(unique(data$approxtime), 1)[1] 30 200 600data$approxtime <- as.factor(data$approxtime) %>% fct_recode("0 hour" = "30", "1 hour" = "200", "6 hour" = "600")
data$strain <- fct_recode(data$strain, "high fidelity" = "wild" , "error prone" = "mutant")
data <-
data %>%
mutate(Venus = FL1.A,
mScarlet = FL4.A,
ratio = Venus/mScarlet)
cv <- function(x) return(round(sd(x)/mean(x),2))
CVs <- data %>% group_by(treatment, strain) %>% dplyr::summarise(across(where(is_double), cv))
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
pivot_longer(cols = c(Venus, mScarlet, ratio),
names_to = "parameter",
values_to = "value")
kernel_plot <- ggplot(data = data,
mapping = aes(x = ratio,
color = treatment, linetype = strain)) +
geom_density() + coord_cartesian(x = c(0.5,2)) +
facet_grid(approxtime~.) + theme_test() +
scale_color_viridis_d(option = "D", end = .75, direction = -1) +
labs(x = "Venus-IAA17/mScarlet ratio", linetype = "polymerase")
kernel_plot
ggsave("orthorep_degradation.pdf", height = 4, width = 4)ggsave("orthorep_degradation.png", height = 4, width = 4)
Gating Strategy
data <- aplate1[wells]To gate out the high FSC-A debris we will use only the lower 99.5% of the data
g <- gate_quantile(fr = data[[1]], channel = "FSC.A", probs = 0.995)
autoplot(data[[1]], x = "FSC-A") + geom_gate(g)
Subset(data[[1]], !g)flowFrame object 'A05.fcs'
with 9950 cells and 14 observables:
name desc range minRange maxRange
$P1 FSC.A FSC-A 16777216 0 16777216
$P2 SSC.A SSC-A 16777216 0 16777216
$P3 FL1.A FL1-A 16777216 0 16777216
$P4 FL2.A FL2-A 16777216 0 16777216
$P5 FL3.A FL3-A 16777216 0 16777216
... ... ... ... ... ...
$P10 FL2.H FL2-H 16777216 0 16777216
$P11 FL3.H FL3-H 16777216 0 16777216
$P12 FL4.H FL4-H 16777216 0 16777216
$P13 Width Width 16777216 0 16777216
$P14 Time Time 16777216 0 16777216
161 keywords are stored in the 'description' slotautoplot(Subset(data[[3]], !g), x = "FSC-A", "SSC-A")
autoplot(data[[3]], "FSC-A", "SSC-A") + geom_gate(g)
Now we need to gate out only singlet cells
chnl <- c("FSC-A", "FSC-H")
singlets <- gate_singlet(x = Subset(data[[1]], !g), area = "FSC.A",height = "FSC.H", prediction_level = 0.999, maxit = 20)
autoplot(data[[1]], "FSC-A", "FSC-H") + geom_gate(singlets)
Now we can assess this singlets gate over for several frames
length(data)[1] 12autoplot(data, x = "FSC-A", y = "FSC-H") + geom_gate(singlets) + facet_wrap("name", ncol = 4) 
autoplot(Subset(data, !g) %>% Subset(singlets), x = "FL1-A") + facet_wrap("name", ncol = 4) 
autoplot(Subset(data, !g |singlets), x = "FSC-A", y = "SSC-A") + facet_wrap("name", ncol = 4)
This looks very consistent across the course of this experiment. We can summarize this gating across the whole experiment, but will not show this here.
#summary(filter(data, !g & singlets))
data <- Subset(data, !g & singlets)Plot Fluorescence vs. Time
dat_sum <- summarizeFlow(data, gated = TRUE)[1] "Summarizing all events..."ggplot(data = dat_sum, aes(x = time, y = FL1.Amean/FL4.Amean, color = factor(strain), linetype = factor(treatment)))+
geom_line() + xlab("Time post first reading (min)") +
ylab("Reporter Fluorescence, FL1 (AU)") + geom_text(aes(label = name))
ggplot(data = dat_sum, aes(x = time, y = conc, color = factor(strain), linetype = factor(treatment)))+
geom_line() + xlab("Time post first reading (min)") +
ylab("Reporter Fluorescence, FL1 (AU)")
Define a gate containing ~95% of the untreated cells expressing the wildtype polymerase.
logt <- estimateLogicle(data[["E05.fcs"]], channels = c("FL4.A", "FL1.A"))
data <- transform(data[c("E05.fcs", "E06.fcs", "E07.fcs", "E08.fcs")], logt)
untreated <- gate_flowclust_2d(data[["E05.fcs"]], xChannel = "FL4.A", yChannel = "FL1.A", K = 1, quantile = 0.90, filterId = "untreated")
treated <- gate_flowclust_2d(data[["E06.fcs"]], xChannel = "FL4.A", yChannel = "FL1.A", K = 1, quantile = 0.90, filterId = "treated")
ggcyto(data, aes(x = "FL4.A", y = "FL1.A")) +
geom_point(color = "green4", alpha = 0.1, size = 0.5) +
ggthemes::theme_base() +
labs(x = "mScarlet-I (free FP)", y = "Venus-IAA17") +
facet_grid(fct_rev(strain) %>%
fct_recode("wild-type" = "wild")~treatment) +
coord_cartesian(xlim = c(1.4,2.6), ylim = c(1.4,2.6)) +
geom_gate(untreated, colour = "magenta", linetype = 2) +
geom_gate(treated, colour = "magenta") +
geom_stats(adjust = c(0.1, 0.05), size = 4.5, alpha = 0.5)
ggsave("sorting-strategy.pdf", height = 4.5, width = 6)ggsave("sorting-strategy.png", height = 4.5, width = 6)
Mutation frequency calculation
As an overestimate, these cells were cultured for ~12 generations over 24 hours. So this results in 212 cells for each initial.
AFB2 is 1725 bps, and the mutation rate of the error prone polymerase is calculated to be \(1 \times 10^{-5}\) substitutions per base. We will assume there is only 1 copy of the p1 plasmid per cell. We will also assume withing this coding sequence, for every 2.1 substitutions (sub) there is 1 nonsynonymous substitution (nsub), per the average across the codon table, not factoring in codon usage across TIR1.
\[ \begin{array}{c|c|c} 1 \times 10^{-5} \text{ sub} & 1725 \text{ bases} * 2 \text{ (bp)} & 1 \text{ nsub} \\ \hline \text{base} & \text{cell} & 2.1 \text{ sub} \end{array} = 0.02 \frac{\text{nsub}}{\text{cell}} \] So on the low end, ~0.0164286 percent of cells have a nonsynonymous substitution in TIR1. But because this substitution rate is really compounding over 12 generations, this estimate is quite low.
Based on this baseline rate per cell (or generation, cell duplication) \(r\), we can then compound this over \(t\) generations, to find the compounded rate \(r_c\). \[ r_c = (1 + r)^t-1 = (1+0.0164286)^{12}-1 = 0.2159686 \] And on the high end, which is a more accurate measure, ~22% of our population contains a nonsynonymous substitution in TIR1.
Session Info
sessionInfo()R Under development (unstable) (2022-10-30 r83209)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.2.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] agricolae_1.3-5 ggthemes_4.2.4 patchwork_1.1.2 wesanderson_0.3.6 flowClust_3.37.0
[6] flowStats_4.11.0 ggcyto_1.27.4 flowWorkspace_4.11.0 ncdfFlow_2.45.0 BH_1.78.0-0
[11] openCyto_2.11.1 gridExtra_2.3 drc_3.0-1 MASS_7.3-58.1 lubridate_1.9.2
[16] forcats_1.0.0 purrr_1.0.1 readr_2.1.4 tibble_3.1.8 tidyverse_2.0.0
[21] tidyr_1.3.0 dplyr_1.1.0 ggridges_0.5.4 stringr_1.5.0 ggplot2_3.4.1
[26] flowTime_1.23.1 flowCore_2.11.0
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.14 magrittr_2.0.3 TH.data_1.1-1 rainbow_3.7
[6] farver_2.1.1 ragg_1.2.5 zlibbioc_1.45.0 vctrs_0.5.2 RCurl_1.98-1.9
[11] htmltools_0.5.4 plotrix_3.8-2 haven_2.5.2 deSolve_1.34 hdrcde_3.4
[16] pracma_2.4.2 KernSmooth_2.23-20 plyr_1.8.8 sandwich_3.0-2 zoo_1.8-11
[21] mime_0.12 lifecycle_1.0.3 pkgconfig_2.0.3 Matrix_1.5-3 R6_2.5.1
[26] fastmap_1.1.0 shiny_1.7.4 digest_0.6.31 klaR_1.7-1 colorspace_2.0-3
[31] S4Vectors_0.37.3 textshaping_0.3.6 labeling_0.4.2 cytolib_2.11.0 fansi_1.0.3
[36] timechange_0.2.0 abind_1.4-5 compiler_4.3.0 withr_2.5.0 carData_3.0-5
[41] DBI_1.1.3 hexbin_1.28.2 highr_0.9 corpcor_1.6.10 gtools_3.9.4
[46] tools_4.3.0 rrcov_1.7-2 httpuv_1.6.9 glue_1.6.2 IDPmisc_1.1.20
[51] questionr_0.7.8 nlme_3.1-161 promises_1.2.0.1 grid_4.3.0 cluster_2.1.4
[56] generics_0.1.3 gtable_0.3.1 fda_6.0.5 labelled_2.10.0 tzdb_0.3.0
[61] data.table_1.14.6 hms_1.1.2 car_3.1-1 utf8_1.2.2 BiocGenerics_0.45.0
[66] pillar_1.8.1 later_1.3.0 robustbase_0.95-0 splines_4.3.0 lattice_0.20-45
[71] AlgDesign_1.2.1 survival_3.4-0 deldir_1.0-6 ks_1.14.0 RProtoBufLib_2.11.0
[76] tidyselect_1.2.0 RBGL_1.75.0 fds_1.8 miniUI_0.1.1.1 knitr_1.41
[81] stats4_4.3.0 xfun_0.35 Biobase_2.59.0 matrixStats_0.63.0 DEoptimR_1.0-11
[86] stringi_1.7.8 codetools_0.2-18 interp_1.1-3 Rgraphviz_2.43.0 graph_1.77.1
[91] cli_3.6.0 flowViz_1.63.0 systemfonts_1.0.4 xtable_1.8-4 munsell_0.5.0
[96] Rcpp_1.0.9 png_0.1-8 XML_3.99-0.13 parallel_4.3.0 ellipsis_0.3.2
[101] mclust_6.0.0 latticeExtra_0.6-30 jpeg_0.1-10 bitops_1.0-7 viridisLite_0.4.1
[106] mvtnorm_1.1-3 scales_1.2.1 crayon_1.5.2 pcaPP_2.0-3 combinat_0.0-8
[111] rlang_1.0.6 multcomp_1.4-22 mnormt_2.1.1 Biological Systems Engineering, Virginia Tech, Blacksburg, VA , USA, co-first author↩︎
Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA, co-first author↩︎
Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, USA↩︎
Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA↩︎
Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA↩︎
Biochemistry and Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, USA↩︎
Biological Systems Engineering, Fralin Life Sciences Institute, and Translational Plant Sciences Center, Virginia Tech, Blacksburg, VA, USA, wrightrc@vt.edu↩︎
---
title: "Supplmental reproducible analysis for: Genetically encoded, noise-tolerant,
  auxin biosensors in yeast facilitate metabolic engineering and directed evolution"
author:
- "Patarasuda Chaisupa^[Biological Systems Engineering, Virginia Tech, Blacksburg,
  VA , USA, co-first author]"
- "Mahbubur Rahman*^[Biological Systems Engineering, Virginia Tech, Blacksburg, VA,
  USA, co-first author]"
- Sherry B. Hildreth^[Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA,
  USA]
- Saede Moseley^[Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA]
- Chauncey Gatling^[Biological Systems Engineering, Virginia Tech, Blacksburg, VA,
  USA]
- Richard F. Helm^[Biochemistry and Fralin Life Sciences Institute, Virginia Tech,
  Blacksburg, VA, USA]
- R. Clay Wright^[Biological Systems Engineering, Fralin Life Sciences Institute,
  and Translational Plant Sciences Center, Virginia Tech, Blacksburg, VA, USA, wrightrc@vt.edu]
output:
  word_document: default
  html_document:
    df_print: paged
  pdf_document: default
  html_notebook: default
---

```{r setup, message=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, 
                      tidy = TRUE, message = FALSE, 
                      warning = FALSE)
library(flowCore)
library(flowTime)
library(ggplot2)
library(stringr)                        
library(ggridges)
library(dplyr)
library(tidyr)
library(tidyverse)
library(drc)
library(gridExtra)
library(openCyto)
library(ggcyto)
library(flowStats)
library(flowClust)
library(wesanderson)
library(patchwork)
library(ggthemes)
library(agricolae)
```

# Time-course response and ratiometric measurement of the single-fusion biosensors

## Time-course degradation

```{r, eval=FALSE}
# Read in flow sets from 20200611 and 20200614
flowSet <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Data/Mahbub/FlowSets/", pattern = "202006", phenoData = "annotation.txt")
flowSet <- flowSet[which(flowSet@phenoData@data$strain %in% c("T1T1", "A2A2"))]

write.flowSet(flowSet, "flowSets/single-time-course")

```


```{r}
flowSet <- read.flowSet(path =  "flowSets/single-time-course", phenoData = "annotation.txt")
# load gates for this strain/cytometer
load("PSB_Accuri_W303.RData")
data_sum <- summarizeFlow(flowset = flowSet, ploidy = "diploid", only = "singlets")
time0_14 <- data_sum %>% dplyr::filter(name == "21D10.fcs") %>% 
  pull(btime) 
time0_11 <- data_sum %>% dplyr::filter(name == "11G02.fcs") %>% 
  pull(btime) 
data_sum <- 
  data_sum %>% 
    mutate(time = case_when(
      folder == "20200611_AFB_epistasis" ~ 
        .$btime - time0_11, 
      folder == "20200614_AFB_epistasis" ~
        .$btime - time0_14
    ))
```

```{r}
shapes <- c("DMSO" = 1, "IAA" = 16)
lines <- c("DMSO" = 2, "IAA" = 1)

data_sum$ratio <- data_sum$FL1.Amean/data_sum$FL4.Amean
data_sum$Venus <- data_sum$FL1.Amean/10000
data_sum_long <- data_sum %>% 
  dplyr::select(time, treatment, yWL, folder, strain, ratio, Venus) %>%
  pivot_longer(cols = c(ratio, Venus), names_to = "parameter")
deg_plot <- ggplot(data = subset(data_sum_long, yWL == "166"), 
       aes(x = time, y = value, shape = treatment, 
           color = treatment, 
           group = interaction(treatment, folder))) + 
  geom_point() + labs(y = "intensity (AU)", x = "time post auxin addition (min)") +
  facet_wrap(~fct_rev(parameter), scales = "free") + 
  scale_shape_manual(values = shapes) + 
  geom_line(aes(linetype = treatment)) +
  scale_linetype_manual(values = lines) + 
  scale_color_viridis_d(option = "D", end = 0.75, direction = -1) + 
  theme_test()
deg_plot
```

```{r}
data <- flowTime::tidyFlow(flowSet, ploidy = "diploid", only = "singlets")
```

```{r}
# get late time points for one strain
last_reading <- 
  tail(unique(data[which(data$yWL == 166), "name"]),4) %>% head(2)
data <- dplyr::filter(data, yWL == "166" & name %in% last_reading)
# clean this up, cut off zeros
data <- subset(data, FL1.A >1 & FL4.A > 1)
data$FLratio <- data$FL1.A/data$FL4.A
range(data$FL1.A)
#calculate cvs
sd(data$FLratio)/mean(data$FLratio)
range(data$FLratio)
```

## Single-fusion CV plot 

```{r}
#calculate normalized values
data$Venus <- data$FL1.A / median(data$FL1.A)
data$mScarlet <- data$FL4.A / median(data$FL4.A)
data$ratio <- data$FLratio / median(data$FLratio)
# make a tidy, long dataset 
data_long <- data %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") 

# need to also format CVs approriately for annotating
cv <- function(x) return(round(sd(x)/mean(x),2))
CVs <- data %>% group_by(treatment) %>%
  summarise(across(where(is_double), cv))
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
    pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value")

CV_plot <- ggplot(data = data_long, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + coord_cartesian(x = c(0,2)) + 
  labs(x = "median normalized intensity", color = "treatment") + 
  facet_grid(fct_relevel(parameter, "Venus")~.) + theme_test() + 
  geom_text(data = subset(CVs, treatment == "IAA"), 
             aes(label = paste0("CV = ", value)),
             x = 0.3, y = 2) + 
  geom_text(data = subset(CVs, treatment == "DMSO"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.8, y = 2) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1)
CV_plot
```

```{r}
layout <- "
AAAAB
CCCCC
"
CV_plot  + guide_area() + deg_plot + plot_annotation(tag_levels = "A") + 
  plot_layout(guides = "collect", heights = c(5, 2), design = layout) 
ggsave("ratio-deg.pdf", width = 5, height = 5)
ggsave("ratio-deg.png", width = 5, height = 5)
```

# Dose-response curves of dual-fusion AFB2 and TIR1 biosensors

## AFB2-based biosensor (yWL210 AFB2 dual-fusion, single ratiometric construct)

```{r, eval = F}
plate_all_210 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/Combine Data_yWL210/All data/", pattern = "DRA-*") 
```

```{r, eval = F}

annotation <- createAnnotation(yourFlowSet = plate_all_210)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL210_datagated_exJan31.csv')

```

```{r, eval = F}
annotation <-
  read.csv(
    '~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL210_datagated_exJan31.csv'
  )

aplate_all_210 <-
  annotateFlowSet(yourFlowSet = plate_all_210,
                  annotation_df = annotation,
                  mergeBy = 'name')
head(rownames(pData(aplate_all_210)))
head(pData(aplate_all_210))
write.flowSet(aplate_all_210, 
              outdir = "flowSets/AFB2-dual-yWL210-dose-response")
```

```{r}
aplate_all_210 <-
  read.flowSet(path = "flowSets/AFB2-dual-yWL210-dose-response", 
               phenoData = "annotation.txt")

plate_all_210_sum <-
  summarizeFlow(aplate_all_210, channel = NA, gated = TRUE)
```

###Dose-response curve

#### Comparing log-logistic and Weibull models
(Figure 2 in Ritz (2009))
```{r}
fitdrc.m1 <-
  drm(YL1.Amean / BL1.Amean ~ dose, data = plate_all_210_sum, fct = LL.4())
fitdrc.m2 <-
 drm(YL1.Amean / BL1.Amean ~ dose, data = plate_all_210_sum, fct = W1.4())
#fitdrc.m3 <- 
# drm(YL1.Amean/BL1.Amean~dose, data=plate_all_210_sum, fct = W2.4())
#Error in drmOpt(opfct, opdfct1, startVecSc, optMethod, constrained, warnVal, :
#Convergence failed

summary(fitdrc.m1)
summary(fitdrc.m2)
#summary(fitdrc.m3)
```

The 4-parameter log-logistic model has a slightly lower residual standard error. 

```{r}
model.LL4_all_210 <-
  drm(YL1.Amean / BL1.Amean ~ dose,
      data = plate_all_210_sum,
      fct = LL.4(names = c(
        "Slope", "Lower Limit", "Upper Limit", "ED50"
      )))
plot(
  model.LL4_all_210,
  broken = TRUE,
  type = "none",
  lty = 1,
  lwd = 5,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Response Signal"
)
#plot(model.LL4_all_210, broken = TRUE, col = "black", add=TRUE)
plot(
  model.LL4_all_210,
  broken = TRUE,
  type = "confidence",
  col = "black",
  add = TRUE
)
summary(model.LL4_all_210)

```
### Individual replicate dose-response curves

```{r}
replicate1_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "1"),
    fct = LL.4()
  )
replicate2_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "2"),
    fct = LL.4()
  )
replicate3_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "3"),
    fct = LL.4()
  )
replicate4_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "4"),
    fct = LL.4()
  )
replicate5_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "5"),
    fct = LL.4()
  )
replicate6_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "6"),
    fct = LL.4()
  )
replicate7_210 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(plate_all_210_sum, replicate == "7"),
    fct = LL.4()
  )

plot(
  replicate1_210,
  broken = TRUE,
  type = "all",
  col = "dark green",
  lty = 2
)
plot(
  replicate2_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark blue",
  lty = 2
)
plot(
  replicate3_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "yellow",
  lty = 2
)
plot(
  replicate4_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark grey",
  lty = 2
)
plot(
  replicate5_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark orange",
  lty = 2
)
plot(
  replicate6_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "brown",
  lty = 2
)
plot(
  replicate7_210,
  broken = TRUE,
  add = TRUE,
  type = "all",
  col = "dark red",
  lty = 2
)
plot(
  model.LL4_all_210,
  broken = TRUE,
  add = TRUE,
  type = "none",
  lty = 1,
  lwd = 5,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Response Signal"
)
#plot(model.LL4_all_210, broken = TRUE, col = "black", add=TRUE)
plot(
  model.LL4_all_210,
  broken = TRUE,
  type = "confidence",
  col = "black",
  add = TRUE
)

```

## TIR1-based biosensor (yWL209 TIR dual-fusion, single ratiometric construct)

```{r, eval = F}
plate_all_209 <-
  read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/Combine Data_yWL209/", pattern = "DRA-*") 
```

```{r, eval = F}
annotation <- createAnnotation(yourFlowSet = plate_all_209)
write.csv(
  annotation,
  '/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL209.csv'
)
```

```{r, eval = F}
annotation <-
  read.csv(
    '~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/11212022_DRA_overlaydata/overlaydata_annotation_yWL209.csv'
  )

aplate_all_209 <-
  annotateFlowSet(yourFlowSet = plate_all_209,
                  annotation_df = annotation,
                  mergeBy = 'name')
head(rownames(pData(aplate_all_209)))
head(pData(aplate_all_209))

write.flowSet(aplate_all_209, outdir = "flowSets/TIR1-dual-yWL209-dose-response")
```

```{r}
aplate_all_209 <-
  read.flowSet(path = "flowSets/TIR1-dual-yWL209-dose-response", 
               phenoData = "annotation.txt")

dat_sum_overlaydata_209 <-
  summarizeFlow(aplate_all_209, gated = TRUE)
```
###Dose-response curve
```{r}
model.LL4_rep1_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "1"),
    fct = LL.4(names = c(
      "Slope", "Lower Limit", "Upper Limit", "ED50"
    ))
  )
plot(
  model.LL4_rep1_209,
  type = "all",
  col = "red",
  lty = 1,
  lwd = 2,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Red/Green Fluorescent Ratio"
)
plot(
  model.LL4_rep1_209,
  broken = TRUE,
  type = "confidence",
  col = "red",
  add = TRUE
)
print(summary(model.LL4_rep1_209))


model.LL4_rep2_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "2"),
    fct = LL.4(names = c(
      "Slope", "Lower Limit", "Upper Limit", "ED50"
    ))
  )
plot(
  model.LL4_rep2_209,
  type = "all",
  col = "blue",
  lty = 1,
  lwd = 2,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Red/Green Fluorescent Ratio"
)
plot(
  model.LL4_rep2_209,
  broken = TRUE,
  type = "confidence",
  col = "blue",
  add = TRUE
)
print(summary(model.LL4_rep2_209))


model.LL4_rep3_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "3"),
    fct = LL.4(names = c(
      "Slope", "Lower Limit", "Upper Limit", "ED50"
    ))
  )
plot(
  model.LL4_rep3_209,
  type = "all",
  col = "dark green",
  lty = 1,
  lwd = 2,
  xlab = "Extracellular IAA concentration (µM)",
  ylab = "Red/Green Fluorescent Ratio"
)
plot(
  model.LL4_rep3_209,
  broken = TRUE,
  type = "confidence",
  col = "dark green",
  add = TRUE
)

print(summary(model.LL4_rep3_209))



model.LL4_all_209 <-
  drm(YL1.Amean / BL1.Amean ~ dose,
      data = dat_sum_overlaydata_209,
      fct = LL.4(names = c(
        "Slope", "Lower Limit", "Upper Limit", "ED50"
      )))
plot(
  model.LL4_all_209,
  type = "all",
  col = "black",
  lty = 1,
  lwd = 3
)
plot(
  model.LL4_all_209,
  broken = TRUE,
  col = "black",
  add = TRUE
)
plot(
  model.LL4_all_209,
  broken = TRUE,
  type = "confidence",
  col = "black",
  add = TRUE
)
print(summary(model.LL4_all_209))

replicate1_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "1"),
    fct = LL.4()
  )
replicate2_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "2"),
    fct = LL.4()
  )
replicate3_209 <-
  drm(
    YL1.Amean / BL1.Amean ~ dose,
    data = subset(dat_sum_overlaydata_209, replicate == "3"),
    fct = LL.4()
  )


plot(
  replicate1_209,
  broken = TRUE,
  add = TRUE,
  type = "none",
  col = "red",
  lty = 2
)
plot(
  replicate2_209,
  broken = TRUE,
  add = TRUE,
  type = "none",
  col = "blue",
  lty = 2
)
plot(
  replicate3_209,
  broken = TRUE,
  add = TRUE,
  type = "none",
  col = "dark green",
  lty = 2
)

```

## Combined Dose-response curveswith ggplot

```{r}
pm210 <-
  expand.grid(treatment = exp(seq(log(1e-5), log(1e2), length = 1000)))
pm210 <-
  cbind(pm210,
        predict(model.LL4_all_210, newdata = pm210, interval = "confidence"))  

plot210 <-
  ggplot(plate_all_210_sum, aes(x = dose, y = YL1.Amean / BL1.Amean))  + 
  scale_x_log10() + geom_ribbon(
    data = pm210,
    aes(
      x = treatment,
      y = Prediction,
      ymin = Lower,
      ymax = Upper
    ),
    alpha = 0.4
  ) + geom_line(data = pm210,
                aes(x = treatment, y = Prediction),
                linewidth = 1.2) + ylab("mScarlet-I/Venus") + xlab("Auxin (IAA, µM)") + 
  scale_x_log10(labels = 
                  scales::label_number(drop0trailing = TRUE)) +  
  scale_color_viridis_c() +
  geom_point(aes(color = replicate), size = 1, alpha = 0.6) + 
  theme_classic() + 
  theme(legend.position = "none", 
        plot.title = element_text(hjust = 0.5),
        plot.title.position = "plot") + 
  ggtitle("AFB2")  
plot210


pm209 <-
  expand.grid(treatment = exp(seq(log(1e-5), log(1e2), length = 1000)))
pm209 <-
  cbind(pm209,
        predict(model.LL4_all_209, newdata = pm209, interval = "confidence"))

plot209 <-
  ggplot(dat_sum_overlaydata_209, aes(x = dose, y = YL1.Amean / BL1.Amean))  + 
  scale_x_log10() + geom_ribbon(
    data = pm209,
    aes(
      x = treatment,
      y = Prediction,
      ymin = Lower,
      ymax = Upper
    ),
    alpha = 0.4
  ) + geom_line(data = pm209,
                aes(x = treatment, y = Prediction),
                linewidth = 1.2) + 
  ylab("mScarlet-I/Venus") + 
  xlab("Auxin (IAA, µM)") + 
  scale_x_log10(labels = 
                  scales::label_number(drop0trailing = TRUE)) + 
  scale_color_viridis_c() +
  geom_point(aes(color = replicate), size = 1, alpha = 0.6) +
  theme_classic() + 
  theme(legend.position = "none", 
        plot.title = element_text(hjust = 0.5),
        plot.title.position = "plot") + 
  #ylim(0.095, 0.12) + 
  ggtitle("TIR1")
plot209

plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A")

ggsave(
  plot = plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A"),
  filename = "dose-response.pdf",
  width = 6,
  height = 3
)
ggsave(
  plot = plot210 + plot209 + patchwork::plot_annotation(tag_levels = "A"),
  filename = "dose-response.png",
  width = 6,
  height = 3
)
```

# LCMS measurements of intracellular auxin

```{r, eval=FALSE}
plate_08052022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/08052022_yWL210_DRA-LCMS-R2/Alldata/", pattern = "DRA*") 
```

```{r, eval=FALSE}
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/08052022_yWL210_DRA-LCMS-R2/08052022_alldata_annotation.csv')

aplate_08052022 <- annotateFlowSet(yourFlowSet = plate_08052022, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_08052022)))
head(pData(aplate_08052022))
write.flowSet(aplate_08052022, "flowSets/AFB2-dual-DR-LCMS")
```
```{r}
aplate_08052022 <- read.flowSet(path = "flowSets/AFB2-dual-DR-LCMS",
                                phenoData = "annotation.txt")
plate_08052022_sum <- summarizeFlow(aplate_08052022, gated = TRUE)
model.LL4_08052022<- drm(YL1.Amean/BL1.Amean~dose, data=subset(plate_08052022_sum, reading =="10"), fct=LL.4(names = c("Slope", "Lower Limit", "Upper Limit", "ED50")))
summary(model.LL4_08052022)
pm_08052022 <- expand.grid(treatment=exp(seq(log(1e-3), log(1e2), length=1000))) 
pm_08052022 <- cbind(pm_08052022,predict(model.LL4_08052022, newdata=pm_08052022, interval="confidence"))  

plot_08052022 <- 
  ggplot(data=subset(plate_08052022_sum, reading =="10"), 
         aes(x = dose, y = YL1.Amean/BL1.Amean))  + 
  geom_ribbon(data = pm_08052022, 
              aes(x = treatment, y = Prediction, ymin = Lower,
                  ymax=Upper), alpha=0.4) + 
  geom_line(data = pm_08052022, 
            aes(x = treatment, y = Prediction)) + 
  xlab("Extracellular IAA (µM)") +
  ylab("AFB2-mScarlet-/Venus-IAA17") + 
  scale_x_log10(labels = 
                  scales::label_number(drop0trailing = TRUE)) +  
  geom_point(color = "black") + 
  theme_classic(base_size = 12) +
  theme(legend.position = "none") 
plot_08052022

LCMSdata <- read.csv('08052022_yWL210_DRA-LCMS_data.csv')

model.LL4_08052022_LCMS<- drm(Observed_IAA_in_uM~Dose_IAA, data=LCMSdata, fct=LL.4(names = c("Slope", "Lower Limit", "Upper Limit", "ED50")))
summary(model.LL4_08052022_LCMS)

plot(model.LL4_08052022_LCMS, type="all",col="black",lty=1, lwd=3, xlab = "Extracellular concentrations of IAA (uM)", ylab = "Observed IAA (uM)")

pm_08052022_LCMS <- expand.grid(treatment=exp(seq(log(1e-3), log(5e2), length=1000))) 
pm_08052022_LCMS <- cbind(pm_08052022_LCMS,
                          predict(model.LL4_08052022_LCMS,
                                  newdata=pm_08052022_LCMS,
                                  interval="confidence")) 
LCMS_intracellular <- 
ggplot(data = LCMSdata, 
       mapping = aes(x = Dose_IAA, 
                     y = Observed_IAA_in_uM)) + 
  geom_point() + 
  geom_ribbon(data = pm_08052022_LCMS, 
              aes(x = treatment, y = Prediction, ymin = Lower,
                  ymax=Upper), alpha=0.4) + 
  geom_line(data = pm_08052022_LCMS, 
            aes(x = treatment, y = Prediction)) +
  scale_x_log10(labels = 
                scales::label_number(drop0trailing = TRUE)) +
  theme_classic(base_size = 13) +
  theme(legend.position = "none") + 
  ylab("Intracellular IAA (µM)") + 
  xlab("Extracellular IAA (µM)")  

plot_08052022 + LCMS_intracellular + 
  plot_annotation(tag_levels = "A")
ggsave("LCMS-comparison.pdf", width=6, height = 3)
ggsave("LCMS-comparison.png", width=6, height = 3)
```

# Auxin-induced degradation time-course assay for the dual-fusion biosensors in different yeast strains

Two isolated colonies from each strain were selected at random and tested in auxin-induced protein degradation assay. IAA working solution was added to obtain the IAA concentration at 50 µM in each culture. The assay was carried out using ThermoFisher Attune NxT B/Y flow cytometer.

Strains:

-   yWL185 (TIR1 dual-fusion in W303)
-   yWL186 (AFB2 dual-fusion in W303)
-   yWL209 (TIR1 dual-fusion in YPH499)
-   yWL210 (AFB2 dual-fusion in YPH499)

```{r, eval = FALSE}
plate_03112022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/10readings/onlyPatstrains/", pattern = "TCA*") 
```

```{r, eval = F}
annotation <- createAnnotation(yourFlowSet = plate_03112022)
write.csv(annotation,'~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/03112022_Time-course assay_10platesPatStrains2.csv')
```

```{r, eval = F}
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/03112022_Time-course assay/03112022_Time-course assay_10platesPatStrains2.csv')
aplate_03112022 <- annotateFlowSet(yourFlowSet = plate_03112022, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_03112022)))
head(pData(aplate_03112022))

write.flowSet(aplate_03112022, outdir = "flowSets/dual-time-course")
```

```{r}
aplate_03112022 <-
  read.flowSet(path = "flowSets/dual-time-course/", phenoData = "annotation.txt")
plate_03112022_sum <- summarizeFlow(aplate_03112022, gated = TRUE)


plate_03112022_sum <-
  plate_03112022_sum %>% mutate(
    background_p = case_when(
      strain %in% c("yWL185", "yWL186") ~ "W303",
      strain %in% c("yWL209", "yWL210") ~ "YPH499"
    ),
    receptor_p = case_when(
      strain %in% c("yWL185", "yWL209") ~ "TIR1",
      strain %in% c("yWL186", "yWL210") ~ "AFB2"
    )
  )
```

```{r}
#The time auxin addition is equal to time zero
time0 <-
  "303112022-Pat-TCA03_Time-course assay_Auxin_yWL185-C1.fcs"
# or whatever well was being read when auxin was added

plate_03112022_sum$time <-
  plate_03112022_sum$btime - 
  plate_03112022_sum[[which(plate_03112022_sum$name == time0), "btime"]]
#single bracket -->extracting the all name, 2 brackets extract just single "value" or "values"

```

```{r}
#To normalize data

plate_03112022_sum <-
  plate_03112022_sum %>% mutate(ratio = BL1.Amean/YL1.Amean) %>%
  group_by(background_p, receptor_p) %>% 
  mutate(normalizedratio = ratio/mean(ratio))
```

```{r}
ratio <- 
  ggplot(data = subset(plate_03112022_sum), 
         aes(x = time, y = normalizedratio,  
             group = interaction(factor(colony), factor(treatment)), 
             linetype =  factor(treatment), shape = factor(treatment), 
             color = factor(treatment))) +
  geom_point(aes(color = treatment), size = 2) + 
  geom_line(aes(color = treatment), linewidth= 1)  + 
  xlab("Time post auxin addition (min)") + 
  scale_shape_manual(values = c(19, 1)) + 
  ylab("Normalized Venus/mScarlet-I") + 
  facet_grid(background_p~receptor_p) +
  scale_color_manual(values = c("#5499C7", "#999999")) + 
  theme_base() + 
  theme(legend.position = "none", 
        panel.grid.minor = element_line(linewidth = 0.3, 
                                        linetype = 'solid', 
                                        colour = "white"), 
        axis.line = element_line(colour = "black", 
                                 linewidth = 1, linetype = "solid")) 
ratio
```

Without normalization

```{r}
ratio_raw <- 
  ggplot(data = subset(plate_03112022_sum), 
         aes(x = time, y = ratio,  
             group = interaction(factor(colony), factor(treatment)), 
             linetype =  factor(treatment), shape = factor(treatment), 
             color = factor(treatment))) +
  geom_point(aes(color = treatment), size = 2) + 
  geom_line(aes(color = treatment), linewidth= 1)  + 
  xlab("Time post auxin addition (min)") + 
  scale_shape_manual(values = c(19, 1)) + 
  ylab("Venus/mScarlet-I") + 
  facet_grid(background_p~receptor_p, scales = "free") +
  scale_color_manual(values = c("#5499C7", "#999999")) + 
  theme_base() + 
  theme(legend.position = "none", 
        panel.grid.minor = element_line(linewidth = 0.3, 
                                        linetype = 'solid', 
                                        colour = "white"), 
        axis.line = element_line(colour = "black", 
                                 linewidth = 1, linetype = "solid")) 
ratio_raw
```

```{r}
ratio_raw / ratio + plot_annotation(tag_levels = "A") & theme(plot.background = element_blank())
ggsave("tc-strains.pdf", width = 5, height = 7)
ggsave("tc-strains.png", width = 5, height = 7)
```


```{r}
#normalize green and red fluorescent expression
plate_03112022_sum <-
  plate_03112022_sum %>% 
  mutate(green = BL1.Amean) %>%
  group_by(background_p, receptor_p) %>% 
  mutate(normalized_Greenexpression = BL1.Amean/mean(BL1.Amean)) %>%
  mutate(red = YL1.Amean) %>%
  mutate(normalized_Redexpression = YL1.Amean/mean(YL1.Amean))

normgreen <- qplot(x = time, y = normalized_Greenexpression, data = plate_03112022_sum, group = interaction(factor(colony), factor(treatment)), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment), size = 2) + geom_line(aes(color = treatment), linewidth = 1) + xlab("Time post first reading (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Normalized Venus expression")+ facet_grid(receptor_p~background_p) + scale_color_manual(values = c("#F1C40F", "#85929E"))+ theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", linewidth = 1, linetype = "solid")) 

normgreen

normred <-qplot(x = time, y = normalized_Redexpression, data = plate_03112022_sum, group = interaction(factor(colony), factor(treatment)), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment), size = 2) + geom_line(aes(color = treatment), linewidth = 1) + xlab("Time post first reading (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Normalized mScarlet expression")+ facet_grid(receptor_p~background_p) + scale_color_manual(values = c("#EC7063", "#616A6B")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", linewidth = 1, linetype = "solid")) 
normred 

green <- qplot(x = time, y = BL1.Amean, data = plate_03112022_sum, group = interaction(factor(colony), factor(treatment)), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment), size = 2) + geom_line(aes(color = treatment), linewidth = 1) + xlab("Time post first reading (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Normalized Venus expression")+ facet_grid(receptor_p~background_p) + scale_color_manual(values = c("#F1C40F", "#85929E"))+ theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", linewidth = 1, linetype = "solid")) 
green

red <-qplot(x = time, y = YL1.Amean, data = plate_03112022_sum, group = interaction(factor(colony), factor(treatment)), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment), size = 2) + geom_line(aes(color = treatment), linewidth = 1) + xlab("Time post first reading (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Normalized mScarlet expression")+ facet_grid(receptor_p~background_p) + scale_color_manual(values = c("#EC7063", "#616A6B")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", linewidth = 1, linetype = "solid")) 
red 

conc <-qplot(x = time, y = conc, data = plate_03112022_sum, group = interaction(factor(colony), factor(treatment)), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment), size = 2) + geom_line(aes(color = treatment), linewidth = 1) + xlab("Time post first reading (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Normalized mScarlet expression")+ facet_grid(receptor_p~background_p) + scale_color_manual(values = c("black", "#616A6B")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", linewidth = 1, linetype = "solid")) 
conc 
```

# CV plot

Using the above time course dataset we can compare coefficients of variation in the individual parameters and the ratio for each of the strains and biosensors at steady state.

## yWL185 (TIR1 dual-fusion, W303 yeast)

```{r}

data185 <- steadyState(aplate_03112022, gated = TRUE)
data185 <- subset(data185, strain == "yWL185" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL185-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL185-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data185 <- subset(data185, BL1.A >1 & YL1.A > 1)
data185$FLratio <- data185$BL1.A/data185$YL1.A
range(data185$BL1.A)
cv <- function(x) return(round(sd(x)/mean(x),2))

data185$Venus <- data185$BL1.A / median(data185$BL1.A)
data185$mScarlet <- data185$YL1.A / median(data185$YL1.A)
data185$FLratio <- data185$BL1.A / data185$YL1.A
data185$ratio <- data185$FLratio / median(data185$FLratio)



data_long185 <- data185 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV185 <-
  data185 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data185

plot185 <- ggplot(data = data_long185, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV185, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 2, y = 1) + 
  geom_text(data = subset(CV185, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) 




venus185 <- ggplot(data185, aes(x=data185$Venus, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5) + labs(x = "Venus", y ="Density") + xlim(-0.1,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV185, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV185, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))
#venus185

red185 <- ggplot(data185, aes(x=data185$mScarlet, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5) + labs(x = "mScarlet", y ="Density") + xlim(-0.1,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV185, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV185, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))
#red185


ratio185<- ggplot(data185, aes(x=data185$ratio, group=treatment, fill=treatment, color= treatment)) + geom_density(adjust=1.5, alpha=.5)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(-0.1,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")   +  geom_text(data = subset(CV185, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.4, y = 2) + 
  geom_text(data = subset(CV185, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.6, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))

#ratio185

plot185 <- grid.arrange(venus185, red185, ratio185, nrow=3, ncol=1)
plot185

```

## yWL186 (AFB2 dual-fusion, W303 yeast)

```{r}
data186 <- steadyState(aplate_03112022, gated = TRUE)
data186 <- subset(data186, strain == "yWL186" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL186-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL186-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data186 <- subset(data186, BL1.A >1 & YL1.A > 1)
data186$FLratio <- data186$BL1.A/data186$YL1.A
range(data186$BL1.A)
cv <- function(x) return(round(sd(x)/mean(x),2))

data186$Venus <- data186$BL1.A / median(data186$BL1.A)
data186$mScarlet <- data186$YL1.A / median(data186$YL1.A)
data186$FLratio <- data186$BL1.A / data186$YL1.A
data186$ratio <- data186$FLratio / median(data186$FLratio)



data_long186 <- data186 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV186 <-
  data186 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data186

plot186 <- ggplot(data = data_long186, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV186, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 2, y = 1) + 
  geom_text(data = subset(CV186, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) 



venus186 <- ggplot(data186, aes(x=data186$Venus, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus (normalized median)", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none") + geom_text(data = subset(CV186, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV186, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))


red186 <- ggplot(data186, aes(x=data186$mScarlet, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet (normalized median)", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none") +  scale_fill_manual(values=c("#EC7063", "#999999")) + geom_text(data = subset(CV186, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV186, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))


ratio186<- ggplot(data186, aes(x=data186$ratio, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")   +  geom_text(data = subset(CV186, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.2, y = 2) + 
  geom_text(data = subset(CV186, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.3, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))

plot186 <- grid.arrange(venus186, red186, ratio186, nrow=3, ncol=1)
plot186
```

## yWL209 (TIR1 dual-fusion, YPH499 yeast)

```{r}

data209 <- steadyState(aplate_03112022, gated = TRUE)
data209 <- subset(data209, strain == "yWL209" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL209-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL209-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data209 <- subset(data209, BL1.A >1 & YL1.A > 1)
data209$FLratio <- data209$BL1.A/data209$YL1.A
range(data209$BL1.A)
cv <- function(x) return(round(sd(x)/mean(x),2))

data209$Venus <- data209$BL1.A / median(data209$BL1.A)
data209$mScarlet <- data209$YL1.A / median(data209$YL1.A)
data209$FLratio <- data209$BL1.A / data209$YL1.A
data209$ratio <- data209$FLratio / median(data209$FLratio)



data_long209 <- data209 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV209 <-
  data209 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data209

plot209 <- ggplot(data = data_long209, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV209, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1) + 
  geom_text(data = subset(CV209, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) 




venus209 <- ggplot(data209, aes(x=data209$Venus, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV209, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV209, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))


red209 <- ggplot(data209, aes(x=data209$mScarlet, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV209, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.1) + 
  geom_text(data = subset(CV209, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.55, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))


ratio209<- ggplot(data209, aes(x=data209$ratio, group=treatment, fill=treatment, color = treatment)) + geom_density(adjust=1.5, alpha=.4)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")   +  geom_text(data = subset(CV209, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.3, y = 2) + 
  geom_text(data = subset(CV209, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.6, y = 2) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))


plot209 <- grid.arrange(venus209, red209, ratio209, nrow=3, ncol=1)
plot209


```


## yWL210 (AFB2 dual-fusion, YPH499 yeast)

```{r}

data210 <- steadyState(aplate_03112022, gated = TRUE)
#data <- tidyFlow(aplate_20210619_W303)

data210 <- subset(data210, strain == "yWL210" & name %in% c("803112022-Pat-TCA08_Time-course assay_Auxin_yWL210-C1.fcs", "803112022-Pat-TCA08_Time-course assay_Control_yWL210-C1.fcs" ))

cv <- function(x) return(round(sd(x)/mean(x),2))


data210 <- subset(data210, BL1.A >1 & YL1.A > 1)
data210$FLratio <- data210$BL1.A/data210$YL1.A
range(data210$BL1.A)
cv <- function(x) return(round(sd(x)/mean(x),2))

data210$Venus <- data210$BL1.A / median(data210$BL1.A)
data210$mScarlet <- data210$YL1.A / median(data210$YL1.A)
data210$FLratio <- data210$BL1.A / data210$YL1.A
data210$ratio <- data210$FLratio / median(data210$FLratio)



data_long210 <- data210 %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio, strain) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") %>%
  dplyr::mutate(parameter = fct_relevel(parameter, "Venus")) 

# need to also format CVs approriately for annotating

CV210 <-
  data210 %>% group_by(treatment) %>% 
  dplyr::summarise(across(dplyr::where(is_double), cv)) %>%
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(
    cols = c(Venus, mScarlet, ratio),
    names_to = "parameter",
    values_to = "value"
  )

#data210

plot210 <- ggplot(data = data_long210, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment")  + theme_test() + 
  geom_text(data = subset(CV210, treatment == "50 uM Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 2, y = 1) + 
  geom_text(data = subset(CV210, treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0, y = 1) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) +
  facet_grid(parameter~.) 




venus210 <- ggplot(data210, aes(x=data210$Venus, group=treatment,color=treatment,  fill=treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "Venus", y ="Density") + xlim(0,2)+ ylim(0,2) + theme_classic() +  theme(legend.position="none") +  geom_text(data = subset(CV210, parameter == "Venus" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.5, y = 1) + 
  geom_text(data = subset(CV210, parameter == "Venus"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.7, y = 1.5) + scale_color_manual(values=c("#F1C40F", "#626567")) + scale_fill_manual(values=c("#F1C40F", "#626567"))


red210 <- ggplot(data210, aes(x=data210$mScarlet, group=treatment,color=treatment,  fill=treatment)) + geom_density(adjust=1.5, alpha=.4) + labs(x = "mScarlet", y ="Density") + xlim(0,2) + ylim(0,2) + theme_classic() +  theme(legend.position="none") + geom_text(data = subset(CV210, parameter == "mScarlet" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 1.2, y = 1.2) + 
  geom_text(data = subset(CV210, parameter == "mScarlet"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 0.4, y = 1.5) + scale_color_manual(values=c("#CB4335", "#626567")) + scale_fill_manual(values=c("#CB4335", "#626567"))


ratio210<- ggplot(data210, aes(x=data210$ratio, group=treatment, color=treatment, fill = treatment)) + geom_density(adjust=1.5, alpha=.4)  +labs(x = "Venus/mScarlet ratio", y ="Density") + xlim(0,2) + ylim(0,3.5) + theme_classic() +  theme(legend.position="none")  + geom_text(data = subset(CV210, parameter == "ratio" & treatment == "50 uM Auxin"), aes(label = paste0("CV = ", value)),
             x = 0.2, y = 1.8) + 
  geom_text(data = subset(CV210, parameter == "ratio"& treatment == "Control"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.6, y = 1.8) + scale_color_manual(values=c("#5499C7", "#626567")) + scale_fill_manual(values=c("#5499C7", "#626567"))
#ratio210

plot210 <- grid.arrange(venus210, red210, ratio210, nrow=3, ncol=1)
plot210

```

```{r}
dual_fusion_cv <- (venus210 + ggtitle("AFB2")+ 
                theme(plot.title = element_text(hjust = 0.5)) | 
                venus209 + ggtitle("TIR1") + 
                theme(plot.title = element_text(hjust = 0.5))) / 
               (red210 | red209) / 
             (ratio210 | ratio209) + 
  theme(title = element_text(hjust = 0))
dual_fusion_cv 
ggsave("dual_fusion_cv.pdf", width = 6.3, height = 6)
ggsave("dual_fusion_cv.png", width = 6.3, height = 6)
```

# Single-fusion vs dual-fusion comparison in W303 yeast strain

-   yWL161 (TIR1 single-fusion, W303 yeast)
-   yWL162 (AFB2 single-fusion, W303 yeast)
-   yWL185 (TIR1 dual-fusion, W303 yeast)
-   yWL186 (AFB2 dual-fusion, W303 yeast)

```{r, eval=FALSE}
plate_20210619_W303 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/W303only/", pattern = "r*") 
```

```{r, eval = F}
annotation <- createAnnotation(yourFlowSet = plate_20210619_W303)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_annotation.csv')
```

```{r, eval=FALSE}
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_annotation.csv')
aplate_20210619_W303 <- annotateFlowSet(yourFlowSet = plate_20210619_W303, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_20210619_W303)))
head(pData(aplate_20210619_W303))
write.flowSet(aplate_20210619_W303, outdir = "flowSets/design-relative-expression")
```

```{r}
aplate_20210619_W303 <- read.flowSet(path = "flowSets/design-relative-expression/", phenoData = "annotation.txt")
plate_20210619_W303_sum <- summarizeFlow(aplate_20210619_W303,  gated = TRUE)


#The time auxin addition is equal to time zero
time0 <- "4E01.fcs" 
# or whatever well was being read when auxin was added

plate_20210619_W303_sum$time <- plate_20210619_W303_sum$btime-plate_20210619_W303_sum[[which(plate_20210619_W303_sum$name == time0), "btime"]]
#single bracket -->extracting the all name, 2 brackets extract just single "value" or "values"

```

```{r}
plate_20210619_W303_sum <- plate_20210619_W303_sum %>%
  mutate(
    design = case_when(
      strain %in% c("yWL185","yWL186") ~ "dual-fusion",
    strain %in% c("yWL161","yWL162") ~ "single-fusion"), 
         fbox = case_when(
    strain %in% c("yWL161","yWL185") ~ "TIR1",
    strain %in% c("yWL162","yWL186") ~ "AFB2")) %>%
  mutate(design_construct = paste(fbox, design)) 

plate_20210619_W303_sum <-
  plate_20210619_W303_sum %>% mutate(ratio = FL1.Amean/FL4.Amean) %>%
  group_by(design, fbox) %>% 
  mutate(normalizedratio = ratio/mean(ratio))

plate_20210619_W303_sum<-
  plate_20210619_W303_sum %>% mutate(green = FL1.Amean) %>%
  group_by(design, fbox) %>% 
  mutate(normalized_Greenexpression = FL1.Amean/mean(FL1.Amean))

plate_20210619_W303_sum <-
  plate_20210619_W303_sum %>% mutate(red = FL4.Amean) %>%
  group_by(design, fbox) %>% 
  mutate(normalized_Redexpression = FL4.Amean/mean(FL4.Amean))
```

```{r}
TIR1_single <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL161"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  xlab("Time (min)") +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  +  scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 


AFB2_single <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL162"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
   xlab("Time (min)") +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none",  panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 

TIR1_dual <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL185"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  + xlab("Time (min)") + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 

AFB2_dual <- qplot(x = time, y = FL1.Amean/FL4.Amean, data = subset(plate_20210619_W303_sum, strain =="yWL186"), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) +
   xlab("Time (min)") +
  geom_point(aes(color = treatment, shape = treatment), size = 1) + geom_line(aes(color = treatment, shape = treatment), linewidth = 0.5)  + scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet ratio") + scale_color_manual(values = c("#5499C7", "#626567")) + theme_minimal() + theme(legend.position = "none", panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white")) +facet_wrap(~design_construct, scale ="free_y") 

grid.arrange(AFB2_single, TIR1_single, TIR1_dual, AFB2_dual, nrow=2, ncol=4)
```

```{r}
W303ratio <- qplot(x = time, y = normalizedratio, data = subset(plate_20210619_W303_sum), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) + xlab("Time (min)") +
  geom_point(aes(color = treatment), size = 0.5) + geom_line(aes(color = treatment), linewidth = 1)  +  scale_shape_manual(values = c(19, 1)) + ylab("Venus") + scale_color_manual(values = c("#2E86C1", "#626567"))  + theme_minimal() + theme(legend.position = "bottom",  panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", 
                      linewidth = 1, linetype = "solid")) + facet_wrap(~design_construct, scale ="free_y")

W303venus <- qplot(x = time, y = normalized_Greenexpression, data = subset(plate_20210619_W303_sum), group = factor(treatment), linetype =  factor(treatment), shape = factor(treatment), color = factor(treatment)) + xlab("Time (min)") +
  geom_point(aes(color = treatment), size = 0.5) + geom_line(aes(color = treatment), linewidth = 1)  +  scale_shape_manual(values = c(19, 1)) + ylab("Venus/Scarlet") + scale_color_manual(values = c("#F1C40F", "#626567"))  + theme_minimal() + theme(legend.position = "bottom",  panel.grid.major = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), panel.grid.minor = element_line(linewidth = 0.3, linetype = 'solid', colour = "white"), axis.line = element_line(colour = "black", 
                      linewidth = 1, linetype = "solid")) + facet_wrap(~design_construct, scale ="free_y")

grid.arrange(W303venus, W303ratio, nrow=2, ncol=2)

```

```{r}
Venus_aov <- aov(FL1.Amean ~ design_construct * treatment * before_after, 
  data = plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200))
summary(Venus_aov)
(Venus_HSD.test <- 
    agricolae::HSD.test(Venus_aov, 
                        trt = c("design_construct", 
                                "treatment", 
                                "before_after"), 
                        console = TRUE))
Venus_groups <- Venus_HSD.test$groups %>% 
  as_tibble(rownames = "names") %>%
  separate(col = names, into = c("design_construct", 
                                "treatment", 
                                "before_after"), 
           sep = "\\:", remove = FALSE) %>% 
  left_join(Venus_HSD.test$means %>% 
              as_tibble(rownames = "names") %>% 
              dplyr::select(c(names, Max)), 
            by = "names") %>%
  mutate(treatment = fct_rev(treatment), 
         before_after = fct_rev(before_after))

```

```{r}
mScarlet_aov <- aov(FL4.Amean ~ design_construct * treatment * before_after, 
  data = plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200))
summary(mScarlet_aov)
(mScarlet_HSD.test <- 
    agricolae::HSD.test(mScarlet_aov, 
                        trt = c("design_construct", 
                                "treatment", 
                                "before_after"), 
                        console = TRUE))
mScarlet_groups <- mScarlet_HSD.test$groups %>% 
  as_tibble(rownames = "names") %>%
  separate(col = names, into = c("design_construct", 
                                "treatment", 
                                "before_after"), 
           sep = "\\:", remove = FALSE) %>% 
  left_join(mScarlet_HSD.test$means %>% 
              as_tibble(rownames = "names") %>% 
              dplyr::select(c(names, Max)), 
            by = "names") %>%
  mutate(treatment = fct_rev(treatment), 
         before_after = fct_rev(before_after))

```

```{r}
boxvenus <- 
  plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200) %>%
  ggplot(aes(
           x = fct_rev(before_after),
           y = FL1.Amean/1000,
         )) +
  geom_boxplot(
    aes(fill = fct_rev(treatment)), alpha = 0.7,
    outlier.shape = NA, show.legend = FALSE) + 
  geom_point(aes(color = fct_rev(treatment)),
             position = position_dodge2(width = .55), 
             size = 1) +
  geom_text(data = Venus_groups, 
            mapping = aes(y = 1.05*Max/1000, label = groups), 
             position = position_dodge2(width = 1),
            color = "black") + 
  scale_fill_manual(values = c("#8f8f8f", "#edc215")) +
  scale_color_manual(values = c("#8f8f8f", "#edc215")) +
  ylab("Venus") + 
  xlab("50 µM Auxin treatment") +  
  facet_wrap( ~ fbox, scale = "free_y") + 
  facet_grid( ~ design_construct) + 
  theme_classic() + labs(color = "") 


boxmScarlet <-
  plate_20210619_W303_sum %>%
  dplyr::filter(time <= 0 | time >= 200) %>%
  ggplot(aes(
           x = fct_rev(before_after),
           y = FL4.Amean/1000
         )) +
  geom_boxplot(aes(fill = fct_rev(treatment)), alpha = .7,
               outlier.shape = NA, show.legend = FALSE) + 
  geom_point(aes(color = fct_rev(treatment)),
             position = position_dodge2(width = .55), 
             size = 1) +
   geom_text(data = mScarlet_groups, 
            mapping = aes(y = 1.2*Max/1000, label = groups), 
             position = position_dodge2(width = 1),
            color = "black") + 
  scale_fill_manual(values = c("#8f8f8f", "#EC7063")) +
  scale_color_manual(values = c("#8f8f8f", "#EC7063")) +
  ylab("mScarlet-I") + 
  xlab("50 µM Auxin treatment") +   
  facet_wrap( ~ fbox, scale = "free_y") + 
  facet_grid( ~ design_construct, scales = "free_y") +
  theme_classic() + labs(color = "") + scale_y_log10()


guide_area() / boxvenus / boxmScarlet + 
  plot_annotation(tag_levels = "A") + 
  plot_layout(guides = "collect", heights = c(.3, 1,1)) & 
  theme(legend.box = "horizontal", 
        legend.spacing = unit(0, "pt"), 
        legend.justification = "top", 
        legend.margin = margin(), 
        legend.box.spacing = unit(0, "pt"), 
        legend.background = element_blank(), 
        legend.box.background = element_blank(), 
        legend.title = element_blank(), 
        legend.key = element_blank())

ggsave("relative-expression-box.pdf", width = 6, height = 5)
ggsave("relative-expression-box.png", width = 6, height = 5)
```

```{r}


```


```{r}
plate_20210619_read3and12 <- read.flowSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/W303only_read3and12/", alter.names = TRUE) 
```

```{r, eval = F}
annotation <- createAnnotation(yourFlowSet = plate_20210619_read3and12)
write.csv(annotation,'/Users/patchaisupa/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_read3and12_annotation.csv')
```

```{r}
annotation <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Time course assays/20210619/20210619_W303only_read3and12_annotation.csv')
aplate_20210619_read3and12<- annotateFlowSet(yourFlowSet = plate_20210619_read3and12, annotation_df = annotation, mergeBy = 'name')
head(rownames(pData(aplate_20210619_read3and12)))
head(pData(aplate_20210619_read3and12))

```

```{r}
W303_read3and12 <- summarizeFlow(aplate_20210619_read3and12,  gated = TRUE)
W303_read3and12 <- W303_read3and12 %>% 
  mutate(design = str_extract(design_construct, "(?<=\\s).*")) %>%
  mutate(design = str_extract(design_construct, ".*(?=\\s)")) 

venus3and12_unnorm <- 
  ggplot(W303_read3and12, 
         aes(x=design_construct, 
             y=FL1.Amean, 
             alpha = fct_rev(before_after), 
             group=treatment)) +
  geom_point(aes(colour = factor(treatment), 
                 shape = factor(treatment)), size = 2,
             position = position_jitterdodge(
               dodge.width = 0.7, 
               jitter.width = 0.5)) + 
  scale_color_manual(values=c("#F1C40F", "#5F6A6A")) +
  ylab("Venus") + theme_classic() +
  theme(axis.text.x = element_text(angle=45, hjust = 1)) + 
  scale_alpha_manual(values = c(0.3, 0.8))

mScarlet3and12_unnorm  <- 
  ggplot(W303_read3and12, 
         aes(x=design_construct, 
             y=FL4.Amean, 
             alpha = fct_rev(before_after), 
             group=treatment)) +
  geom_point(aes(colour = factor(treatment), 
                 shape = factor(treatment)), 
             size = 2, 
             position = position_jitterdodge(
               dodge.width = .7, 
               jitter.width =.5)) + 
  scale_color_manual(values=c("#E74C3C", "#5F6A6A")) +
  ylab("mScarlet-I") + theme_classic() + 
  theme(axis.text.x = element_text(angle=45, hjust = 1)) + 
  scale_alpha_manual(values = c(0.3, 0.8))

guide_area() / (venus3and12_unnorm | mScarlet3and12_unnorm) + 
  plot_layout(guides = "collect", 
              heights = c(1, 3))  +
  plot_annotation(tag_levels = "A") &
  theme(legend.position='top', legend.direction = "vertical",
        legend.title = element_blank(), 
        axis.title.x = element_blank(), 
        legend.justification = "left", 
        legend.box.just = "left", 
        legend.margin = margin())
ggsave("relative-expression.png", width = 4, height = 3)
ggsave("relative-expression.pdf", width = 4, height = 3)
```

Coefficient of variation (CV) analysis

```{r}

data <- steadyState(aplate_20210619_W303, gated = TRUE)
#data <- tidyFlow(aplate_20210619_W303)

data <- subset(data, strain == "yWL161" & name %in% c("11L01.fcs", "11L07.fcs"))

#range(data$FL1.A)
#sd(data$FLratio)/mean(data$FLratio)
#range(data$FLratio)
data <- subset(data, FL1.A >1 & FL4.A > 1)
data$FLratio <- data$FL1.A/data$FL4.A
range(data$FL1.A)
#calculate cvs
sd(data$FLratio)/mean(data$FLratio)
range(data$FLratio)
cv <- function(x) return(round(sd(x)/mean(x),2))

#calculate normalized values
data$Venus <- data$FL1.A / median(data$FL1.A)
data$mScarlet <- data$FL4.A / median(data$FL4.A)
data$ratio <- data$FLratio / median(data$FLratio)
CVs <- data %>% group_by(treatment) %>%
  summarise(across(where(is_double), cv))
# make a tidy, long dataset 

data_long <- data %>% 
  dplyr::select(treatment, Venus, mScarlet, ratio) %>%
  pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value") 
# need to also format CVs appropriately for annotating
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
    pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value")

#data
CV_plot <- ggplot(data = data_long, 
       mapping = aes(x = value, 
                     color = treatment)) + 
  geom_density() + xlim(c(-1,4)) + 
  labs(x = "median normalized intensity", color = "treatment") + 
  facet_grid(parameter~.) + theme_test() + 
  geom_text(data = subset(CVs, treatment == "Auxin"), 
             aes(label = paste0("CV = ", value)),
             x = 0.1, y = 0.6) + 
  geom_text(data = subset(CVs, treatment == "Control EtOH"), 
             aes(label = paste0("CV = ", value)), 
            x = 1.8, y = 0.6) + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1)
CV_plot

```

# Auxin prodcution in stationary phase

Read in annotated flowSet
```{r, eval=FALSE}
flowSet <- read.flowSet(path = paste0("~/Google Drive/Shared drives/PlantSynBioLab/Auxin Biosensor Manuscript/Auxin Biosensor Data/FlowSets/", experiment_date, "_", experiment_name), phenoData = "annotation.txt")
write.flowSet(flowSet, "flowSets/auxin-biosynthesis")
```
```{r}
flowSet <- read.flowSet(path = "flowSets/auxin-biosynthesis/", phenoData = "annotation.txt")
```

## Summary Analysis

```{r}
load("PSB_Accuri_W303.RData")
dat_sum <- summarizeFlow(flowSet, ploidy = "haploid", only = "yeast") # These gates might not work well for YPH499, but there was a lot of debris
```

```{r}
dat_sum <- mutate(.data = dat_sum, across(starts_with("FL"), as.numeric)) %>%
  mutate(strain = str_remove(strain, " ratiometric sensor") %>% 
           str_replace(pattern = "in trans", 
                       replacement = "single-fusion") %>%
           str_replace(pattern = "in cis",
                       replacement = "dual-fusion"))
dat_sum$pred_auxin <- dat_sum$FL4.Amean/dat_sum$FL1.Amean 
dat_sum$pred_auxin_SE <- with(dat_sum, 
                              sqrt((FL4.Asd/100/FL4.Amean)^2 +
                              (FL1.Asd/100/FL1.Amean)^2)*pred_auxin)
dat_sum$pred_auxin_sd <- with(dat_sum, 
                              sqrt((FL4.Asd/FL4.Amean)^2 + 
                              (FL1.Asd/FL1.Amean)^2)*pred_auxin)
```

```{r}
ggplot(data = dat_sum, 
       aes(x = fct_reorder(treat, pred_auxin), y = pred_auxin,
           color = factor(strain))) +
  geom_point() + 
  theme(axis.text.x = element_text(angle = 45, 
                                   hjust = 1, vjust = 1)) + 
  facet_grid(.~strain, scales = "free_x") + coord_flip() +
  theme(legend.position = "bottom", 
        legend.direction = "vertical") + 
  labs(x = "", y = "Predicted auxin (mScarlet-I/Venus-IAA17)", 
       color = "strain")
ggsave("strain_sensor_comparison_raw.pdf", width = 8, height = 3)
```

```{r}
dat_sum <- dat_sum %>% group_by(strain) %>% 
  mutate(norm_pred_auxin = 
           pred_auxin / mean(pred_auxin
                             [which(.data$treat == 
                            "aerobic exponential phase")]))

ggplot(data = dat_sum, aes(x = fct_reorder(treat, norm_pred_auxin),
                           y = norm_pred_auxin,
                           color = factor(strain))) +
  geom_point(position = position_jitter(width = 0.2)) + 
  theme(axis.text.x = element_text(angle = 45, 
                                   hjust = 1, vjust = 1)) + 
  coord_flip() + theme(legend.position = "top", 
                       legend.direction = "vertical") + 
  labs(x = "", 
       y = "Normalized Predicted auxin (mScarlet-I/Venus-IAA17)", 
       color = "strain")
ggsave("strain_sensor_comparison_norm.pdf", width = 5, height = 5)

```

Full distributions

```{r}
data <- steadyState(flowset = flowSet, ploidy = "haploid", only = "yeast")

hist(x = log(data$FL1.A, 10))
hist(x = log(data$FL4.A, 10))

```

```{r}
data$pred_auxin <- data$FL4.A/data$FL1.A
data <- data %>% dplyr::filter(FL1.A > 1 & FL4.A > 1)
data$treat <- data$treat %>% str_remove("phase")
```


```{r}
endog_auxin <- ggplot(
  data = subset(
    data,
    strain == "W303 ratiometric sensor AFB2 in cis" &
      pred_auxin < 2),
  aes(
    x = pred_auxin,
    y = fct_reorder(treat, .x = pred_auxin, 
                    .fun = median, .desc = TRUE) %>% 
      fct_relevel("aerobic exponential ", after = Inf),
    fill = factor(stat(quantile))
  )
) +
  stat_density_ridges(
    geom = "density_ridges_gradient",
    calc_ecdf = TRUE,
    quantiles = 4,
    color = "grey",
    alpha = 0.5
  ) +
  scale_fill_viridis_d(name = "Quartiles") +
  theme(legend.position = NULL) +
  #facet_wrap(.~treat) +
  xlim(c(-0.1, 1)) +
  labs(y = NULL, x = "Predicted relative auxin production (AU) \n (AFB2-mScarlet-I/Venus-IAA17)") + theme_ridges() + theme(legend.position = "none")
ggsave("20210626_auxin_production_quartiles.pdf", width = 5, height = 4)
endog_auxin
```

## Biosensor sensitivity at stationary phase

```{r, eval=FALSE}
plate_09282022 <- read.plateSet(path = "~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/09282022_yWL210_DRA-stationary/Data/", pattern = "S-DRA*") 
```

```{r, eval=FALSE}
annotation_09282022 <- read.csv('~/Google Drive/Shared drives/PlantSynBioLab/Pat/Experiments/Does-response assay/09282022_yWL210_DRA-stationary/09282022_stationaryphase_annotation.csv')

aplate_09282022 <- annotateFlowSet(yourFlowSet = plate_09282022, annotation_df = annotation_09282022, mergeBy = 'name')
head(rownames(pData(aplate_09282022)))
head(pData(aplate_09282022))
write.flowSet(aplate_09282022, "flowSets/AFB2-dual-stationary")
```

```{r}
aplate_09282022 <- read.flowSet(path = "flowSets/AFB2-dual-stationary/", phenoData = "annotation.txt")

dat_sumGr_09282022 <- summarizeFlow(aplate_09282022, gated = TRUE)

model.LL4_09282022<- drm(YL1.Amean/BL1.Amean~dose, data=subset(dat_sumGr_09282022, set == "4"), fct=LL.4(names = c("Slope", "Lower Limit", "Upper Limit", "ED50")))

pm210stat <- expand.grid(treatment=exp(seq(log(1e-5), log(1e2), length=1000))) 
pm210stat <- cbind(pm210stat,predict(model.LL4_09282022, newdata=pm210stat, interval="confidence"))  #m2all = model of 210 data

plot210_stat <- ggplot(subset(dat_sumGr_09282022, set == "4"), 
                       aes(x = dose, y = YL1.Amean/BL1.Amean))  +
 geom_ribbon(data = pm210stat, 
             aes(x = treatment, y = Prediction, 
                 ymin = Lower, ymax=Upper), alpha = .2) +
  geom_line(data = pm210stat, aes(x = treatment, y = Prediction)) + 
  ylab("AFB2-mScarlet-I/Venus-IAA17") + 
  xlab("Exogenous Auxin (µM)") + 
  scale_x_log10(labels = 
                  scales::label_number(drop0trailing = TRUE)) + 
  scale_color_viridis_d() + geom_point() + 
  theme_classic(base_size = 14) + 
  theme(legend.position = "none") 
plot210_stat
```

## Combined figure

```{r, fig.width=8, fig.height=4}
endog_auxin + plot210_stat + plot_annotation(tag_levels = "A")
ggsave("auxin-accumulation.pdf", width = 8, height = 4)
ggsave("auxin-accumulation.png", width = 8, height = 4)
```


# Mutant library analysis

Here we aim to test auxin induced Venus-IAA17 degradation relative to the bicistronic mScarlet-I control with AFB2 expressed from the p1 plasmid in the OrthoRep continuous mutagenesis system. 

## Procedure

Temperature 30 degree celcius
Shaking at 250rpm 
DO-URA-HIS, starting volume: 10 mL
Overnight concentration: 30 events/uL
1PM started from 2 colonies

Initial reading ~10AM
Auxin concentration: 100 uM (.1% DMSO)
After 4th reading: B04.fcs

###Importing and annotating data

```{r}
aplate1 <- read.flowSet(path = 'flowSets/OrthoRep', phenoData = "annotation.txt")
dat_sum <- summarizeFlow(aplate1, gated = TRUE)
```

```{r tidyFlow}
data <- flowTime::tidyFlow(aplate1, gated = TRUE)
data <- subset(data, FL4.A > 1)
range(data$FL1.A)
range(data$FL4.A)
data$ratio <- data$FL1.A / data$FL4.A
data <- subset(data, data$ratio < 10)
```
Make a kernel density plot overlapping mutant population with parent, DMSO and IAA that we can then stack a few timepoints with facets. For timepoints it looks like the 2nd, 7th and 12th would be a good demonstration of the timecourse.

```{r}
wells <- dat_sum %>%
                 # Get well/file names of the 2nd, 7th and 12th readings of the 4 strains. 
                 dplyr::slice(1:4 + unlist(lapply((c(2, 7, 12) - 1)*4, rep, 4))) %>%
                 dplyr::pull("name")
data <- 
  dplyr::filter(data, name %in% wells)

data$approxtime <- signif(data$etime, digits = 1)
signif(unique(data$approxtime), 1)
data$approxtime <- as.factor(data$approxtime) %>% fct_recode("0 hour" = "30", "1 hour" = "200", "6 hour" = "600")
data$strain <- fct_recode(data$strain, "high fidelity" = "wild" , "error prone" = "mutant")
data <- 
  data %>% 
  mutate(Venus = FL1.A, 
         mScarlet = FL4.A, 
         ratio = Venus/mScarlet)

cv <- function(x) return(round(sd(x)/mean(x),2))
CVs <- data %>% group_by(treatment, strain) %>% dplyr::summarise(across(where(is_double), cv))
CVs <- CVs %>% dplyr::select(treatment, Venus, mScarlet, ratio) %>%
    pivot_longer(cols = c(Venus, mScarlet, ratio), 
               names_to = "parameter", 
               values_to = "value")

kernel_plot <- ggplot(data = data, 
       mapping = aes(x = ratio, 
                     color = treatment, linetype = strain)) + 
  geom_density() + coord_cartesian(x = c(0.5,2)) + 
  facet_grid(approxtime~.) + theme_test() + 
  scale_color_viridis_d(option = "D", end = .75, direction = -1) + 
  labs(x = "Venus-IAA17/mScarlet ratio", linetype = "polymerase")
kernel_plot
ggsave("orthorep_degradation.pdf", height = 4, width = 4)
ggsave("orthorep_degradation.png", height = 4, width = 4)

```

## Gating Strategy 

```{r}
data <- aplate1[wells]
```

To gate out the high FSC-A debris we will use only the lower 99.5% of the data 
```{r}
g <- gate_quantile(fr = data[[1]], channel = "FSC.A", probs = 0.995)
autoplot(data[[1]], x = "FSC-A") + geom_gate(g)
Subset(data[[1]], !g)
autoplot(Subset(data[[3]], !g), x = "FSC-A", "SSC-A")
```

```{r}
autoplot(data[[3]], "FSC-A", "SSC-A") + geom_gate(g)
```
Now we need to gate out only singlet cells
```{r}
chnl <- c("FSC-A", "FSC-H")
singlets <- gate_singlet(x = Subset(data[[1]], !g), area = "FSC.A",height = "FSC.H", prediction_level = 0.999, maxit = 20)
autoplot(data[[1]], "FSC-A", "FSC-H") + geom_gate(singlets)
```
Now we can assess this singlets gate over for several frames
```{r}
length(data)
autoplot(data, x = "FSC-A", y = "FSC-H") + geom_gate(singlets) + facet_wrap("name", ncol = 4) 

autoplot(Subset(data, !g) %>% Subset(singlets), x = "FL1-A") + facet_wrap("name", ncol = 4) 
```
```{r}
autoplot(Subset(data, !g |singlets), x = "FSC-A", y = "SSC-A") + facet_wrap("name", ncol = 4)
```

This looks very consistent across the course of this experiment. We can summarize this gating across the whole experiment, but will not show this here. 

```{r}
#summary(filter(data, !g & singlets))
data <- Subset(data, !g & singlets)
```


Plot Fluorescence vs. Time

```{r, fig.width = 4, fig.height = 4}
dat_sum <- summarizeFlow(data, gated = TRUE)

ggplot(data = dat_sum, aes(x = time, y = FL1.Amean/FL4.Amean, color = factor(strain), linetype = factor(treatment)))+
geom_line() + xlab("Time post first reading (min)") +
ylab("Reporter Fluorescence, FL1 (AU)") + geom_text(aes(label = name))

ggplot(data = dat_sum, aes(x = time, y = conc, color = factor(strain), linetype = factor(treatment)))+
geom_line() + xlab("Time post first reading (min)") +
ylab("Reporter Fluorescence, FL1 (AU)")
```
Define a gate containing ~95% of the untreated cells expressing the wildtype polymerase. 

```{r}
logt <- estimateLogicle(data[["E05.fcs"]], channels = c("FL4.A", "FL1.A"))
data <- transform(data[c("E05.fcs", "E06.fcs", "E07.fcs", "E08.fcs")], logt)
untreated <- gate_flowclust_2d(data[["E05.fcs"]], xChannel = "FL4.A", yChannel = "FL1.A", K = 1, quantile = 0.90, filterId = "untreated")
treated <- gate_flowclust_2d(data[["E06.fcs"]], xChannel = "FL4.A", yChannel = "FL1.A", K = 1, quantile = 0.90, filterId = "treated")



ggcyto(data, aes(x = "FL4.A", y = "FL1.A")) + 
  geom_point(color = "green4", alpha = 0.1, size = 0.5) + 
  ggthemes::theme_base() + 
  labs(x = "mScarlet-I (free FP)", y = "Venus-IAA17") +
  facet_grid(fct_rev(strain) %>% 
               fct_recode("wild-type" = "wild")~treatment) + 
  coord_cartesian(xlim = c(1.4,2.6), ylim = c(1.4,2.6)) + 
  geom_gate(untreated, colour = "magenta", linetype = 2) + 
  geom_gate(treated, colour = "magenta") +
  geom_stats(adjust = c(0.1, 0.05), size = 4.5, alpha = 0.5) 


ggsave("sorting-strategy.pdf", height = 4.5, width = 6)
ggsave("sorting-strategy.png", height = 4.5, width = 6)
```


## Mutation frequency calculation
 
As an overestimate, these cells were cultured for ~12 generations over 24 hours. So this results in 2^12^ cells for each initial. 

AFB2 is 1725 bps, and the mutation rate of the error prone polymerase is calculated to be $1 \times 10^{-5}$ substitutions per base. We will assume there is only 1 copy of the p1 plasmid per cell. We will also assume withing this coding sequence, for every 2.1 substitutions (sub) there is 1 nonsynonymous substitution (nsub), per the average across the codon table, not factoring in codon usage across TIR1.

$$
    \begin{array}{c|c|c}
     1 \times 10^{-5} \text{ sub}  & 
     1725 \text{ bases} * 2 \text{ (bp)} & 
     1 \text{ nsub} \\ \hline
      \text{base}                              & 
      \text{cell} & 
      2.1 \text{ sub}
    \end{array} = `r 10^-5 *2100*2/2.1` \frac{\text{nsub}}{\text{cell}} 
$$
So on the low end, ~`r (cell_n_rate <- 10^-5 *1725*2/2.1)` percent of cells have a nonsynonymous substitution in *TIR1*. But because this substitution rate is really compounding over 12 generations, this estimate is quite low. 

Based on this baseline rate per cell (or generation, cell duplication) $r$, we can then compound this over $t$ generations, to find the compounded rate $r_c$.
$$
r_c = (1 + r)^t-1 = (1+`r cell_n_rate`)^{12}-1 = 
  `r (1+cell_n_rate)^12 - 1`
$$
And on the high end, which is a more accurate measure, ~`r round(100*((1+cell_n_rate)^12 - 1), 0)`% of our population contains a nonsynonymous substitution in *TIR1*. 

# Session Info 
```{r}
sessionInfo()
```

